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The True Colours Remote Symptom Monitoring System: A Decade
of Evolution
Sarah M Goodday1,2*, PhD; Lauren Atkinson1,3, MSc; Guy Goodwin1,4, FMedSci; Kate Saunders1,4, DPhil, MRCPsych,
FHEA; Matthew South1, PhD; Clare Mackay1,4, PhD; Mike Denis1, MSc; Chris Hinds1,5, PhD; Mary-Jane
Attenburrow1,4*, MBBS, MRCPsych; Jim Davies5,6, PhD; James Welch5,7, BA (Oxon); William Stevens6, PhD; Karen
Mansfield1, PhD; Juulia Suvilehto1, PhD; John Geddes1,4, MD, MRCPsych
1Department of Psychiatry, University of Oxford, Oxford, United Kingdom
24YouandMe, Seattle, WA, United States
3Oxford Center for Human Brain Activity, University of Oxford, Oxford, United Kingdom
4Oxford Health NHS Foundation Trust, Oxford, United Kingdom
5Big Data Institute, University of Oxford, Oxford, United Kingdom
6Department of Computer Science, University of Oxford, Oxford, United Kingdom
7NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
*these authors contributed equally
Corresponding Author:
Sarah M Goodday, PhD
Department of Psychiatry
University of Oxford
Warneford Lane
Oxford, OX3 7JX
United Kingdom
Phone: 44 (0)1865 618200
Email: sarah.goodday@psych.ox.ac.uk
Abstract
The True Colours remote mood monitoring system was developed over a decade ago by researchers, psychiatrists, and software
engineers at the University of Oxford to allow patients to report on a range of symptoms via text messages, Web interfaces, or
mobile phone apps. The system has evolved to encompass a wide range of measures, including psychiatric symptoms, quality of
life, and medication. Patients are prompted to provide data according to an agreed personal schedule: weekly, daily, or at specific
times during the day. The system has been applied across a number of different populations, for the reporting of mood, anxiety,
substance use, eating and personality disorders, psychosis, self-harm, and inflammatory bowel disease, and it has shown good
compliance. Over the past decade, there have been over 36,000 registered True Colours patients and participants in the United
Kingdom, with more than 20 deployments of the system supporting clinical service and research delivery. The system has been
adopted for routine clinical care in mental health services, supporting more than 3000 adult patients in secondary care, and 27,263
adolescent patients are currently registered within Oxfordshire and Buckinghamshire. The system has also proven to be an
invaluable scientific resource as a platform for research into mood instability and as an electronic outcome measure in randomized
controlled trials. This paper aimed to report on the existing applications of the system, setting out lessons learned, and to discuss
the implications for tailored symptom monitoring, as well as the barriers to implementation at a larger scale.
(J Med Internet Res 2020;22(1):e15188) doi: 10.2196/15188
KEYWORDS
symptom assessment; signs and symptoms; digital health; ecological momentary assessment; mood disorders
Introduction
The advancement of digital technology will gradually continue
to shape how we measure, monitor, and manage health. A wide
range of digital symptom monitoring tools exist, but there is a
lack of evidence regarding their effectiveness in a health care
context, particularly in the area of mental health. Such evidence
will arise only from studies involving significant usage,
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conducted in close partnership with clinicians, patients, and
managers. For example, digital tools for patient-reported
outcome measures (PROMs) are becoming standard practice in
randomized controlled trials (RCTs) in many areas [1], and
meta-analyses [2,3] have confirmed their equivalence with
paper-based approaches.
True Colours is a digital tool, developed over a decade ago by
psychiatrists, software engineers, and researchers at the
University of Oxford, which has achieved significant usage.
The initial version was used for remote monitoring of mood
disorders, allowing patients and their clinicians to record and
review symptom change. The recognized need to capture and
monitor higher frequency phenotype information, particularly
for conditions such as bipolar disorder (BD), is not new. Hard
copy symptom monitoring diaries have been used for decades.
However, these are limited by practicality issues.
The True Colours system has many advantages over paper-based
approaches toward the capturing of detailed, timed phenotype
information, including the following: the ability to prompt for
contemporaneous input, the automatic calculation of summary
scores, the visualization of changes over time, and the provision
of real time, as well as historical data to support clinical review,
assessment, and early intervention. From a research perspective,
the tool has additional advantages: eliminating errors in the
transcription of information from paper forms, supporting a
higher frequency of prompted, directed phenotyping, and
reducing the recall bias associated with the recording of
symptoms. Subsequent versions of the tool have added new
functionality for data entry, patient or cohort management, and
research delivery.
The system has been applied across several patient, participant,
and high-risk populations, being used across 21 unique research
and clinical service settings in the Oxfordshire and
Buckinghamshire regions in the United Kingdom. Over the past
decade, there have been over 36,000 registered True Colours
participants from whom over 1.4 million questionnaire responses
have been collected. Several feasibility studies and clinical
service applications support the potential of True Colours as a
larger scale symptom monitoring system, an electronic PROM,
and a tool for digital phenotyping. This paper aimed to describe
the evolution of the tool, its applications, and achievements and
to discuss the potential for future wider application and
integration.
Research Applications
The True Colours system was originally designed to monitor
mood symptoms in adult patients with BD, attending the BD
Research Clinic at the Department of Psychiatry at the
University of Oxford, and it was designed for use in clinical
trials, evolving from the Oxford University Symptom
Monitoring System [4,5]. The original version of the system
involved automated weekly prompts, delivered by text message
or email (chosen by preference), for patients to complete
self-reported measures of symptoms, including depression
(16-item Quick Inventory of depressive symptoms) [6] and
mania (5-item Altman Self Rating Mania Scale) [7], and other
measures, such as anxiety (Generalized Anxiety Disorder
Scale–7) [8], quality of life (EQ-5D) [9], and lifestyle behaviors.
The system has expanded to include a wide range of symptoms
from validated scales and bespoke measures tailored to specific
research projects. As part of the True Colours platform, total
symptom scores were presented graphically via a secure website
and made available to patients, participants, and clinicians upon
request. Over the past decade, the use of True Colours has
expanded to several different research cohorts and patient
populations (Figure 1).
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Figure 1. Evolution and applications of True Colours. AMoSS: Automated Monitoring of Symptom Severity Study; BD: bipolar disorder; BDRN:
Bipolar Disorder Research Network; CEQUEL: Comparative evaluation of quetiapine plus lamotrigine; COMBO: Collaborative Care Model for Bipolar
Disorder; FIMM: Facilitated Integrated Mood Management; FWwTC: Feeling Well with True Colours; IBD: inflammatory bowel disease; LQD: lithium
versus quetiapine augmentation for treatment resistant depression; MIMM: Manualized Integrated Mood Management; OxBREaD: Oxford Brain Body
Research into Eating Disorders; OxCAMS: Oxford Study of Calcium Channel Antagonism, Cognition, Mood instability and Sleep; OxLith: Oxford
Lithium Trial; RCT: randomized controlled trial.
The OXTEXT Program
Earlier work involving the University of Oxford Symptom
Monitoring System established that technology-assisted
symptom monitoring was acceptable to patients over a period
of 36 weeks with 75% compliance [4], meaning that, on average,
patients reported symptoms in response to prompts 75% of the
time over follow-up. The OXTEXT program was dedicated to
developing and validating the True Colours remote symptom
monitoring system for patients, at a larger scale. Across several
projects, the program revised the software after in-depth patient
consultation, established a cohort of well-characterized patients
with BD by using the improved system, determined the potential
cost-effectiveness of this remote capture tool in clinical service,
and tested remote mood monitoring as a potential intervention
via RCTs (OXTEXT research studies 1-6). Several publications
resulted from these studies, largely from the OXTEXT-1 cohort
comprising up to 367 patients (≥16 years of age) from
Oxfordshire, with a Diagnostic and Statistical Manual of Mental
Disorders-IV diagnosis of BD (BDI, BDII, or BD-not otherwise
specified), with some patients completing up to 81 months of
continuous weekly mood measures. Compliance and
acceptability of the True Colours system in the OXTEXT-1
cohort were excellent, with low attrition (<2%) and a median
of less than 8% of weeks of missing data that did not differ by
key sociodemographic factors or by mood score [10]. This pilot
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work has demonstrated support for the feasibility of True
Colours as a remote mood monitoring system in patients with
mood disorders [4,10], and it has lent important insights into
detectable mood instability that differentiates clinical course
[5] and other BD patient characteristics, including cognitive
functioning [10,11].
The OXTEXT-2 study [12] assessed participant’s compliance
with monitoring, their mental health resource use (including
hospitalizations and face-to-face and phone contact with mental
health staff), and service and medication costs, before and during
their first 12 months of engagement with True Colours.
Compliance with monitoring was high, with a median response
completion rate of 92% for both Web-based and SMS symptom
reporting and all patients continuing to report during the duration
of the study. The introduction of True Colours was thought
likely to reduce service costs, but this was not supported in
OXTEXT-2. In fact, when associated with enhanced specialist
care, medication costs increased over the first year of
monitoring. This illustrated that studies of any digital addition
to care need to account for all possible confounders relating to
mood monitoring and mental health service costs. OXTEXT-2
did not examine nonmental health service costs, and larger
economic evaluations of the True Colours system are required
and are being conducted.
The True Colours system was also utilized as part of a
psycho-education intervention for 121 patients with BD in an
RCT (OXTEXT-6) [13]. The Facilitated Integrated Mood
Management (FIMM) [14] study condition involved True
Colours mood monitoring, a psycho-education manual, and
individual sessions with a facilitator. This was compared with
Manualized Integrated Mood Management, which only involved
the psycho-education manual. Patients in the FIMM arm showed
better knowledge of BD, and greater BD knowledge was
associated with a high number of months in remission over
1-year follow-up [13]. Of note, True Colours in isolation is not
intended as an intervention, but it may improve symptoms via
insight into patients about their symptoms and closer, more
accurate monitoring by clinicians, which will require further
study.
The OXTEXT-7 study commenced in 2013 involving a trial
rolled out to all 11 community mental health treatment services
across Oxfordshire and Buckinghamshire titled as Feeling Well
with True Colours (FWwTC). The goal of FWwTC was to offer
patients a self-monitoring system that could allow care
interventions to be tailored to the individual. Patients and
clinicians create tailored symptom monitoring schedules on the
basis of the type of symptom measure, frequency of prompts
(weekly, daily, and several times a day), and reminder
frequency. This study was a stepped-wedge, cluster randomized
design. In this design, all services eventually implemented
FWwTC, but the time at which they were trained to implement
FWwTC was randomized to compare outcomes in treatment
services before and after the introduction of FWwTC. The aim
of this phase of OXTEXT was to apply True Colours to other
patient populations (including those experiencing depression,
anxiety, psychosis, alcohol and drug use, and BD) and test the
feasibility and cost-effectiveness of such a tool in a larger scale
secondary care setting. Experience from this trial is currently
being synthesized, and it has proved heuristically useful [15],
although uptake across clinical services was a challenge,
illustrating the considerable barriers to innovation that persist
in the National Health Service and other medical services.
Digital Phenotyping Studies
Digital phenotyping is the individual-level high-resolution data
capture enabled by digital devices. The promise in this data
capture is its ability to collect passive or active information in
a real-world setting unbound to clinical visits. This affords the
opportunity to discover new trajectories of signs and symptoms
of disease, resulting in refined phenotypes and better detection
and management of illness. The Collaborative Network for
Bipolar Research to Improve Outcomes (ConBrio) [16] was a
translational research program aimed at bringing together basic
and clinician scientists in mathematics, computational biology,
cognitive neuroscience, and neuroimaging. Central to the
ConBrio program is the use of True Colours complemented by
other methods for deep and frequent mood phenotyping to
accelerate understanding and treatment of BD. This program
has supported several projects, such as the Automated
Monitoring of Symptom Severity Study (AMoSS), the use of
True Colours in several RCTs, for example, Oxford Study of
Calcium Channel Antagonism, Cognition, Mood instability and
Sleep (OxCaMS) and Oxford Lithium Trial (OxLith), and other
large phenotyping studies from the BD Research Network
(BDRN) [17].
Automated Monitoring of Symptom Severity Study
Taking advantage of the developments in digital technology
and ubiquity of mobile phones, the AMoSS study introduced a
mobile phone app, Mood Zoom, to facilitate a higher frequency
of symptom monitoring and included wearable devices as
measures of objective symptoms. The Mood Zoom questionnaire
comprises mood state descriptor items that are rated on a scale
from 1 to 7 [18], which could be completed several times a day.
Mood Zoom was used alongside weekly True Colours mood
monitoring to help understand, in greater detail, mood episodes
and mood instability in patients with BD and borderline
personality disorder, as well as healthy volunteers in a sample
of 139 patients with 3 months of continuous data (as per
protocol) but with over 12 months of continuous data (for those
willing to continue). The introduction of a mobile phone app
also enabled the collection of passive background data, such as
number of texts or calls and geolocation [19], which could
reflect proxies of behavior associated with BD and how they
are associated with mood, an emerging area with promise for
the identification of behavioral markers of impending BD-related
episodes [20]. Quantitative [18,21-23] studies have supported
the feasibility and acceptability of the use of the Mood Zoom
app and True Colours for daily and weekly symptom monitoring
in patients with BD, borderline personality disorder, and
controls. Specifically, attrition was low in the AMoSS cohort,
with only 1 subject withdrawing and 8 subjects being excluded
because of providing data for less than 2 months. Median
adherence for the Mood Zoom and weekly measures was greater
than 80% and 85%, respectively, and it remained stable over
the study follow-up [18]. A qualitative study of 20 subjects from
the AMoSS cohort provided support for the fact that reporting
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on symptoms once daily was of no inconvenience, and it was
felt that the system contributed to insights into personal
symptoms and patterns [24]. Additional themes from this study
highlight the importance of tailoring patient preferences into
symptom reporting tools.
In recent studies, additional objective physiological measures,
derived from Fitbit and wrist-worn accelerometers, were
included, along with daily and weekly mood monitoring [22]
as well as the proteus patch [21,25] that provides an estimate
of heart rate. These studies have contributed insights into
detectable variability of sleep patterns in patients with BD and
borderline personality disorder, which map onto observable
symptoms of low and irritable mood [21] and variability in
mood [25]. The additional add-on of wearables offers an exciting
line of inquiry into objective symptoms of illness-alleviating
biases relating to subjective reporting of symptoms. This
potentially supports downstream applications of True Colours,
with the inclusion of additional devices for the measurement of
objective symptoms, which will be important for deeper insights
into early signs of disease.
Other Mood-Related Research Applications
The BDRN [17] adopted the True Colours system, engaging
815 research participants (815/4080, 19.97% of invited existing
BDRN participants) with mood disorders [26]. BDRN
participants with a diagnosis of BDII were more likely to register
with True Colours. Approximately 78.2% (637/815) of
registered participants completed 3 months of symptom
reporting, approximately 51.1% (413/808) of the participants
completed more than 1 year, and some participants continued
mood monitoring for up to 3 years, demonstrating the feasibility
of such a remote mood monitoring system at a larger scale.
An international application of True Colours is from the
Canadian Flourish High-risk Offspring Study [27], recruiting
young offspring of a parent with BD. The Flourish group has
piloted the Web-based True Colours monitoring system in 50
high-risk offspring of a bipolar parent and 108 control offspring
of psychiatrically well parents. Compliance was good over 30
days, with approximately 80% and greater than 90% of high-risk
and control offspring completing daily ratings, respectively,
and no difference in compliance between study groups. Daily
mood scores significantly differentiated the high-risk from
control offspring, and irregularity in weekly mood and anxiety
scores was higher in high-risk offspring with remitted major
mood disorders compared with those with no lifetime history
of major mood disorders [27].
Additional studies from the University of Oxford have made
use of the True Colours system to elucidate mood variability in
BD, involving determining the different nonlinear time series
processes of mood instability and analytic techniques for
appropriately detecting it from high-frequency time series data
[5,28], as well as its associations with mental imagery [29].
Application to Randomized Controlled Trials
RCTs of treatment efficacy in psychiatric disorders are
expensive and lengthy, given the needed follow-up time for full
Diagnostic and Statistical Manual of Mental Disorders threshold
mood episodes to develop. Traditional endpoint assessments
using paper and pencil questionnaires or clinician-rated
diagnostic episodes also ignore clinically significant symptoms
not meeting full diagnostic threshold between episodes [30] and
cognitive dysfunction [31], which could be used to determine
earlier and more proximal treatment effects. Several RCTs have
used True Colours as both primary electronic outcome
assessments and secondary higher frequency outcome
measurements. For example, a 12-week double blind RCT
(CEQUEL) [32] assessed combination therapy with quetiapine
plus lamotrigine versus quetiapine monotherapy plus lamotrigine
placebo on depressive symptoms in 266 patients (≥16 years)
with BD, recruited across 27 different United Kingdom clinics.
Another completed single blind RCT (OASIS) [33] of 3755
university students across the United Kingdom used True
Colours to measure outcomes to determine the effectiveness of
a Web-based cognitive behavioral therapy for insomnia and
other psychiatric symptoms, including psychosis, mood, and
anxiety.
Other mood-related applications of True Colours for outcome
assessment in ongoing RCTs include the OxLith [34], aimed to
compare lithium with placebo on mood instability in adult
patients with BD; a trial assessing the clinical effectiveness and
cost-effectiveness of lithium versus quetiapine augmentation
for treatment-resistant depression [35]; and OxCaMS [36],
which aims to assess the impact of a calcium channel blocker
on cognition and brain activity in adults with mood instability.
Finally, the Oxford Brain Body Research into Eating Disorders
study [37] involves a pilot trial to assess the safety, acceptability,
and feasibility of deep brain stimulation in patients diagnosed
with severe eating disorders. Other funded large trials involving
the True Colours system under development include the
Pramipexole Therapy in Treatment Resistant Depression and
Bipolar Depression (PAX-D and PAX-BD) [38,39].
Expansion to Other Populations
As the research and clinical utility of True Colours became
evident, it naturally branched out to other populations and
research contexts. The Cognition and Mood Evolution across
Time study is aimed at measuring cognition and brain activity
in healthy participants with various levels of mood instability—a
useful application of True Colours, with the inclusion of daily
mood monitoring and cognitive tasks [40].
The True Colours system has also been modified for community
outpatients, with a diagnosis of psychosis using forensic
psychiatric services (FOXWEB risk violence tool). This research
application involved the development of a Web-based violence
risk monitoring tool for psychosis, which provides visual
feedback of patient scores to clinicians to guide risk assessment
[41], and this is being further piloted in inpatients.
The Brief Interventions for Self-Harm (BIRSH) clinic [42] has
piloted True Colours for self-harm prevention in patients (13-65
years) presenting to accident and emergency departments. The
aim of this ongoing research and service evaluation application
was to determine the effectiveness of a new clinical service
incorporating remote symptom monitoring to reduce self-harm
repetition and health service costs.
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The True Colours inflammatory bowel disease (IBD) group has
expanded the True Colours schedule to include daily measures
of ulcerative colitis and Crohn’s disease symptoms, as well as
fortnightly quality-of-life and other validated measures of
disease activity. The initial aim of the True Colours IBD project
was to develop and test the feasibility of a predictive index of
IBD. A 6-month pilot in 66 patients supported the initial
feasibility of this system, with 76% adherence rate for daily
measures and 86% patient retention [43]. Further work has
supported associations between daily IBD symptom measures
and biological measures of disease activity [44], and this has
facilitated the prediction of whether escalation of therapy or
clinical investigation would be needed [45]. Qualitative findings
from this work suggest that patients felt more in control and
empowered by the True Colours IBD system [43].
Clinical Service Applications
Several of the noted research applications have evolved into the
use of True Colours for a purely patient monitoring and/or
clinician monitoring tool, despite little infrastructure and
resources to do so. As of January 10, 2019, almost 3000 patients
with any psychiatric condition and more than 700 clinicians
have registered with True Colours in adult community mental
health treatment service clinics across Oxfordshire and
Buckinghamshire. The uniqueness of this application of remote
monitoring of symptoms is in the individualized approach. This
enables patients to choose, in consultation with their health care
professional, how they would like to self-monitor, directly
aligning from qualitative work suggesting the preference of
flexibility and personalization in a symptom monitoring tool
[24]. The system has also been taken up by child and adolescent
mental health services across the Oxfordshire region, with
27,263 registered users.
True Colours IBD is a prime showcase of what True Colours
could evolve into—an integrated platform for individualized
patient and clinician monitoring of symptoms and quality-of-life
outcomes, with the potential to predict when more symptoms
are expected and prevent unnecessary clinic visits. With further
validation, the implications this model could have for reducing
health care costs and burden on individuals are extensive. Since
September 2019, there are currently more than 750 registered
IBD patients, within the John Radcliffe Hospital in Oxfordshire,
using True Colours as a monitoring tool. True Colours has also
been applied as a patient-reported outcome monitoring tool in
clinical service clinics, testing the effectiveness of Ketamine as
a therapy for treatment-resistant depression [46] and for
self-harm risk assessment as an extension to the ongoing pilot
work conducted by BIRSH [42]. Finally, the Collaborative Care
Model for BD is an ongoing project aimed at testing the
feasibility of True Colours in a primary care setting to
understand perspectives of the True Colours system from both
patients and clinicians. This project also aims to engage different
services (primary and secondary care clinicians) in the
collaborative treatment of patients through the sharing of True
Colours symptom ratings.
Discussion
Over the past decade, True Colours has transformed from a
simple text message prompt and reply system to a personalized
Web-based symptom monitoring tool. This tool is now applied
across a number of clinical populations and is integrated into
several clinics as part of routine clinical care across the
Oxfordshire and Buckinghamshire regions. A small team at the
University of Oxford and the Big Data Institute has been
supporting the continued use of True Colours and its application
across a wide range of settings. Despite the relatively little
resource that has been put into sustaining this system, its
progress and scale, to date, are quite impressive, largely driven
by small independent research grants.
The utility of True Colours as a research tool is unequivocal.
The existing research involving this tool has contributed to
considerable advancements in knowledge of mood instability
and its correlates in mood and personality disorders, which
would not have been possible with traditional aperiodic research
or clinic assessments. The potential linkage of True Colours’
patient-reported data to electronic medical records data currently
available within United Kingdom–Clinical Record Interactive
Search—a national research platform comprising deidentified
electronic patient medical records—could yield a rich source
of high-frequency phenotyping information for future research.
This data linkage could provide continuous measures of
patient-reported symptoms occurring in real time, which could
be mapped onto hospital visits and acute episodes of illness.
This could afford the opportunity to fill in the gaps between
clinic visits and determine early subsyndromal phases of illness
that could reflect targets for prevention of episode recurrence
or worsening of symptoms—a substantial scientific and clinical
resource.
In 2017, there were 325,000 mobile health apps available
internationally, including lifestyle interventions, symptoms
trackers, and personal coaches [47]. A vast majority of these
tools are not evidence based, and their ability to accurately
measure symptoms or feasibly engage patients is largely
unknown [48,49]. Only about 25% of digital health app users
continue using the app after 10 uses [50], indicating challenges
with low retention. Furthermore, with the rapid turnaround of
digital health apps, it is difficult to rigorously test their
effectiveness or implement into practice before they become
obsolete [51]. Other symptom monitoring platforms include the
Chrono-record [52], a computer-based symptom monitoring
system, and the MONitoring treatment and pRediCtion BD
episode system [53], an Android-based mobile phone objective
and subjective symptom monitoring system designed for patients
with BD. Patientslikeme [54] is a digital health platform in the
United Kingdom, which involves a Web-based system that
enables patients to track symptoms and view other members’
health information. The Patientslikeme platform currently has
600,000 registered users, and it is meant to produce data for
research purposes and provide empowerment and community
to patients to track their own symptoms. These tools are useful
in unique ways, but these are yet to have any integration with
clinical service. In addition, they are targeted toward specific
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conditions or the broad reporting of symptoms, some untethered
to validated measures.
In an era where the digital health market is becoming
increasingly saturated, careful integration of these tools within
the health care system is crucial [55]. There is a need to develop
digital remote monitoring tools that are evidence based [56],
with infrastructure to support secure and sensitive personal
information and enable the growth of the tool in tandem with
rapidly developing digital technologies. Obvious barriers to this
potential integration surround buy-in from health care providers,
the potential to create inefficiencies, and data security concerns.
This underscores the needed infrastructure for such a remote
monitoring tool in clinical practice, with education for clinicians
on its purpose and use, an electronic system with ease of access,
and the flexibility and support to tailor the service to different
patient populations and clinical care contexts. Uptake within
clinical service will be a challenge and will require support from
several participating parties.
What is unique about True Colours is the pilot work behind the
tool’s feasibility across different patient populations, and its
use alongside clinical judgement. Its evolution has been guided
by several feasibility studies, clinical and software development
expertise, and, most importantly, participant, patient, and
clinician feedback. The concept of True Colours as an integrated
clinical care model offers benefits to patients through the
returning of simple, visually effective symptom summaries,
empowering individuals to play an active role in their health,
which alone could have a therapeutic effect, as seen in other
areas of medicine, such as oncology [57,58]. For clinical
practice, this tool could enable clinicians to have access to
continuous health information from their patients unbound to
clinic visits, providing PROMs at higher frequencies and lending
insight into dynamic fluctuations in symptoms that cannot be
captured by traditional health measurement systems by
self-report measures recalling symptoms over long periods of
time. In turn, this could support real-time assessment and
management of chronic conditions while freeing up time and
resources for the National Health Service.
Acknowledgments
This work was funded by a Medical Research Council Mental Health Data Pathfinder Award. Additional funding for the True
Colours system has come from the National Institute for Health Research (NIHR) Oxford Health Biomedical Research Center.
This funding body had no role in the design, writing, or interpretation of this paper. The views expressed are those of the authors
and not necessarily those of the National Health Service, NIHR, or Department of Health.
Conflicts of Interest
JRG reports grants from United Kingdom Medical Research Council, grants from Wellcome, grants from NIHR, outside the
submitted work; JRG led the conception of True Colours, a digital phenotyping and outcome assessment tool, and JRG has
overseen its implementation in routine clinical practice and research studies. He is also an NIHR Senior Investigator and Director
of the NIHR Oxford Health Biomedical Research Centre. GG is an NIHR Emeritus Senior Investigator, holding shares in P1Vital
and P1Vital products, and has served as consultant, advisor, or CME speaker in the last 3 years for Allergan, Angelini, Compass
pathways, MSD, Janssen, Lundbeck (/Otsuka or /Takeda), Medscape, Minerva, P1Vital, Pfizer, Sage, Servier, Shire, and Sun
Pharma. All other authors report no conflict of interest related to this paper.
References
1. Byrom B, Gwaltney C, Slagle A, Gnanasakthy A, Muehlhausen W. Measurement Equivalence of Patient-Reported Outcome
Measures Migrated to Electronic Formats: A Review of Evidence and Recommendations for Clinical Trials and Bring Your
Own Device. Ther Innov Regul Sci 2019 Jul;53(4):426-430. [doi: 10.1177/2168479018793369] [Medline: 30157687]
2. Muehlhausen W, Doll H, Quadri N, Fordham B, O'Donohoe P, Dogar N, et al. Equivalence of electronic and paper
administration of patient-reported outcome measures: a systematic review and meta-analysis of studies conducted between
2007 and 2013. Health Qual Life Outcomes 2015 Oct 7;13:167 [FREE Full text] [doi: 10.1186/s12955-015-0362-x] [Medline:
26446159]
3. Gwaltney CJ, Shields AL, Shiffman S. Equivalence of electronic and paper-and-pencil administration of patient-reported
outcome measures: a meta-analytic review. Value Health 2008;11(2):322-333 [FREE Full text] [doi:
10.1111/j.1524-4733.2007.00231.x] [Medline: 18380645]
4. Bopp J, Miklowitz D, Goodwin G, Stevens W, Rendell J, Geddes J. The longitudinal course of bipolar disorder as revealed
through weekly text messaging: a feasibility study. Bipolar Disord 2010 May;12(3):327-334 [FREE Full text] [doi:
10.1111/j.1399-5618.2010.00807.x] [Medline: 20565440]
5. Bonsall MB, Wallace-Hadrill SM, Geddes JR, Goodwin GM, Holmes EA. Nonlinear time-series approaches in characterizing
mood stability and mood instability in bipolar disorder. Proc Biol Sci 2012 Mar 7;279(1730):916-924 [FREE Full text]
[doi: 10.1098/rspb.2011.1246] [Medline: 21849316]
6. Rush A, Trivedi MH, Ibrahim HM, Carmody TJ, Arnow B, Klein DN, et al. The 16-Item Quick Inventory of Depressive
Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients
with chronic major depression. Biol Psychiatry 2003 Sep 1;54(5):573-583. [doi: 10.1016/s0006-3223(02)01866-8] [Medline:
12946886]
J Med Internet Res 2020 | vol. 22 | iss. 1 | e15188 | p. 7https://www.jmir.org/2020/1/e15188 (page number not for citation purposes)
Goodday et alJOURNAL OF MEDICAL INTERNET RESEARCH
XSL
•
FO
RenderX
7. Altman EG, Hedeker D, Peterson JL, Davis JM. The Altman Self-Rating Mania Scale. Biol Psychiatry 1997 Nov
15;42(10):948-955. [doi: 10.1016/S0006-3223(96)00548-3] [Medline: 9359982]
8. Spitzer RL, Kroenke K, Williams JB, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7.
Arch Intern Med 2006 May 22;166(10):1092-1097. [doi: 10.1001/archinte.166.10.1092] [Medline: 16717171]
9. EuroQol Group. EuroQol--a new facility for the measurement of health-related quality of life. Health Policy 1990
Dec;16(3):199-208. [doi: 10.1016/0168-8510(90)90421-9] [Medline: 10109801]
10. McKnight RF, Bilderbeck AC, Miklowitz DJ, Hinds C, Goodwin GM, Geddes JR. Longitudinal mood monitoring in bipolar
disorder: course of illness as revealed through a short messaging service. J Affect Disord 2017 Dec 1;223:139-145. [doi:
10.1016/j.jad.2017.07.029] [Medline: 28753472]
11. Bilderbeck AC, Reed ZE, McMahon HC, Atkinson LZ, Price J, Geddes JR, et al. Associations between mood instability
and emotional processing in a large cohort of bipolar patients. Psychol Med 2016 Nov;46(15):3151-3160. [doi:
10.1017/S003329171600180X] [Medline: 27572660]
12. Simon J, Budge K, Price J, Goodwin G, Geddes J. Remote mood monitoring for adults with bipolar disorder: an explorative
study of compliance and impact on mental health service use and costs. Eur Psychiatry 2017 Sep;45:14-19 [FREE Full
text] [doi: 10.1016/j.eurpsy.2017.06.007] [Medline: 28728090]
13. Bilderbeck AC, Atkinson LZ, McMahon HC, Voysey M, Simon J, Price J, et al. Psychoeducation and online mood tracking
for patients with bipolar disorder: a randomised controlled trial. J Affect Disord 2016 Nov 15;205:245-251. [doi:
10.1016/j.jad.2016.06.064] [Medline: 27454410]
14. Miklowitz DJ, Price J, Holmes E, Rendell J, Bell S, Budge K, et al. Facilitated Integrated Mood Management for adults
with bipolar disorder. Bipolar Disord 2012 Mar;14(2):185-197 [FREE Full text] [doi: 10.1111/j.1399-5618.2012.00998.x]
[Medline: 22420594]
15. Nickless A, Voysey M, Geddes J, Yu L, Fanshawe TR. Mixed effects approach to the analysis of the stepped wedge cluster
randomised trial-Investigating the confounding effect of time through simulation. PLoS One 2018;13(12):e0208876 [FREE
Full text] [doi: 10.1371/journal.pone.0208876] [Medline: 30543671]
16. Dimensions. CON BRIO: Collaborative Network for Bipolar Research to Improve Outcomes. URL: https://app.dimensions.ai/
details/grant/grant.3639647 [accessed 2019-11-20]
17. Bipolar Disorder Research Network (BDRN). URL: http://www.bdrn.org [accessed 2019-11-20]
18. Tsanas A, Saunders K, Bilderbeck A, Palmius N, Osipov M, Clifford G, et al. Daily longitudinal self-monitoring of mood
variability in bipolar disorder and borderline personality disorder. J Affect Disord 2016 Nov 15;205:225-233 [FREE Full
text] [doi: 10.1016/j.jad.2016.06.065] [Medline: 27449555]
19. Palmius N, Tsanas A, Saunders KE, Bilderbeck AC, Geddes JR, Goodwin GM, et al. Detecting bipolar depression from
geographic location data. IEEE Trans Biomed Eng 2017 Aug;64(8):1761-1771 [FREE Full text] [doi:
10.1109/TBME.2016.2611862] [Medline: 28113247]
20. Goodday SM, Cipriani A. Challenges in identifying behavioural markers of bipolar disorder through objective smartphone
data. Aust N Z J Psychiatry 2019 Feb;53(2):168-169. [doi: 10.1177/0004867418816813] [Medline: 30518223]
21. Carr O, Saunders KE, Bilderbeck AC, Tsanas A, Palmius N, Geddes JR, et al. Desynchronization of diurnal rhythms in
bipolar disorder and borderline personality disorder. Transl Psychiatry 2018 Apr 12;8(1):79 [FREE Full text] [doi:
10.1038/s41398-018-0125-7] [Medline: 29643339]
22. Palmius NO, Bilderbeck A, Goodwin G, Saunders K, Tsanas A, Clifford G. A Multi-sensor Monitoring System for Objective
Mental Health Management in Resource Constrained Environments. In: Proceedings of the Appropriate Healthcare
Technologies for Low Resource Settings. 2014 Presented at: AHT'14; September 17-18, 2014; London, UK. [doi:
10.1049/cp.2014.0764]
23. Arribas IP, Goodwin GM, Geddes JR, Lyons T, Saunders KE. A signature-based machine learning model for distinguishing
bipolar disorder and borderline personality disorder. Transl Psychiatry 2018 Dec 13;8(1):274 [FREE Full text] [doi:
10.1038/s41398-018-0334-0] [Medline: 30546013]
24. Saunders K, Bilderbeck A, Panchal P, Atkinson L, Geddes J, Goodwin G. Experiences of remote mood and activity
monitoring in bipolar disorder: a qualitative study. Eur Psychiatry 2017 Mar;41:115-121 [FREE Full text] [doi:
10.1016/j.eurpsy.2016.11.005] [Medline: 28135594]
25. Carr O, Saunders KE, Tsanas A, Bilderbeck AC, Palmius N, Geddes JR, et al. Variability in phase and amplitude of diurnal
rhythms is related to variation of mood in bipolar and borderline personality disorder. Sci Rep 2018 Jan 26;8(1):1649 [FREE
Full text] [doi: 10.1038/s41598-018-19888-9] [Medline: 29374207]
26. Gordon-Smith K, Saunders K, Geddes JR, Harrison PJ, Hinds C, Craddock N, et al. Large-scale roll out of electronic
longitudinal mood-monitoring for research in affective disorders: report from the UK bipolar disorder research network. J
Affect Disord 2019 Mar 1;246:789-793. [doi: 10.1016/j.jad.2018.12.099] [Medline: 30623825]
27. Duffy A, Keown-Stoneman CD, Goodday SM, Saunders K, Horrocks J, Grof P, et al. Daily and weekly mood ratings using
a remote capture method in high-risk offspring of bipolar parents: compliance and symptom monitoring. Bipolar Disord
2019 Mar;21(2):159-167. [doi: 10.1111/bdi.12721] [Medline: 30422376]
28. Moore PJ, Little MA, McSharry PE, Goodwin GM, Geddes JR. Mood dynamics in bipolar disorder. Int J Bipolar Disord
2014 Dec;2(1):11 [FREE Full text] [doi: 10.1186/s40345-014-0011-z] [Medline: 26092397]
J Med Internet Res 2020 | vol. 22 | iss. 1 | e15188 | p. 8https://www.jmir.org/2020/1/e15188 (page number not for citation purposes)
Goodday et alJOURNAL OF MEDICAL INTERNET RESEARCH
XSL
•
FO
RenderX
29. Holmes EA, Deeprose C, Fairburn CG, Wallace-Hadrill SM, Bonsall MB, Geddes JR, et al. Mood stability versus mood
instability in bipolar disorder: a possible role for emotional mental imagery. Behav Res Ther 2011 Oct;49(10):707-713
[FREE Full text] [doi: 10.1016/j.brat.2011.06.008] [Medline: 21798515]
30. Harrison PJ, Geddes JR, Tunbridge EM. The emerging neurobiology of bipolar disorder. Trends Neurosci 2018
Jan;41(1):18-30 [FREE Full text] [doi: 10.1016/j.tins.2017.10.006] [Medline: 29169634]
31. Bourne C, Aydemir Ö, Balanzá-Martínez V, Bora E, Brissos S, Cavanagh JT, et al. Neuropsychological testing of cognitive
impairment in euthymic bipolar disorder: an individual patient data meta-analysis. Acta Psychiatr Scand 2013
Sep;128(3):149-162. [doi: 10.1111/acps.12133] [Medline: 23617548]
32. Geddes JR, Gardiner A, Rendell J, Voysey M, Tunbridge E, Hinds C, CEQUEL Investigators and Collaborators. Comparative
evaluation of quetiapine plus lamotrigine combination versus quetiapine monotherapy (and folic acid versus placebo) in
bipolar depression (CEQUEL): a 2 × 2 factorial randomised trial. Lancet Psychiatry 2016 Jan;3(1):31-39 [FREE Full text]
[doi: 10.1016/S2215-0366(15)00450-2] [Medline: 26687300]
33. Freeman D, Sheaves B, Goodwin GM, Yu L, Nickless A, Harrison PJ, et al. The effects of improving sleep on mental health
(OASIS): a randomised controlled trial with mediation analysis. Lancet Psychiatry 2017 Oct;4(10):749-758 [FREE Full
text] [doi: 10.1016/S2215-0366(17)30328-0] [Medline: 28888927]
34. Saunders KE, Cipriani A, Rendell J, Attenburrow M, Nelissen N, Bilderbeck AC, et al. Oxford Lithium Trial (OxLith) of
the early affective, cognitive, neural and biochemical effects of lithium carbonate in bipolar disorder: study protocol for a
randomised controlled trial. Trials 2016 Mar 2;17(1):116 [FREE Full text] [doi: 10.1186/s13063-016-1230-7] [Medline:
26936776]
35. Marwood L, Taylor R, Goldsmith K, Romeo R, Holland R, Pickles A, et al. Study protocol for a randomised pragmatic
trial comparing the clinical and cost effectiveness of lithium and quetiapine augmentation in treatment resistant depression
(the LQD study). BMC Psychiatry 2017 Jun 26;17(1):231 [FREE Full text] [doi: 10.1186/s12888-017-1393-0] [Medline:
28651526]
36. Atkinson LZ, Colbourne L, Smith A, Harmer CH, Nobre AC, Rendell J, et al. The Oxford study of Calcium channel
Antagonism, Cognition, Mood instability and Sleep (OxCaMS): study protocol for a randomised controlled, experimental
medicine study. Trials 2019 Feb 12;20(1):120 [FREE Full text] [doi: 10.1186/s13063-019-3175-0] [Medline: 30755265]
37. Park RJ, Scaife JC, Aziz TZ. Study protocol: using Deep-Brain stimulation, multimodal neuroimaging and neuroethics to
understand and treat severe enduring anorexia nervosa. Front Psychiatry 2018;9:24 [FREE Full text] [doi:
10.3389/fpsyt.2018.00024] [Medline: 29681866]
38. ISRCTN Registry. Is Pramipexole Effective as an Add-on Treatment for People With Treatment-resistant Depression?URL:
http://www.isrctn.com/ISRCTN84666271 [accessed 2019-11-20]
39. Northern Centre for Mood Disorders. URL: https://mood-disorders.co.uk/ [accessed 2019-11-20]
40. Panchal P, Scholl J, Nelissen N, Saunders K, Darby D, Rushworth M, et al. Mood instability and reward processing: daily
remote monitoring as a modern phenotyping tool for bipolar disorder. Eur Neuropsychopharmacol 2018;28(Suppl 1):S88-S89.
[doi: 10.1016/j.euroneuro.2017.12.121]
41. Gulati G, Cornish R, Al-Taiar H, Miller C, Khosla V, Hinds C, et al. Web-based violence risk monitoring tool in psychoses:
pilot study in community forensic patients. J Forensic Psychol Pract 2016 Jan 1;16(1):49-59 [FREE Full text] [doi:
10.1080/15228932.2016.1128301] [Medline: 26924945]
42. Brand F. FoNS Centre for Nursing Innovation. 2015. Developing, Implementing and Evaluating a Model for a Self Harm
Clinic. URL: https://www.fons.org/Resources/Documents/Project%20Reports/BIRSH-REPORT-June-2015-.pdf [accessed
2019-11-20]
43. Walsh A. Semantic Scholar.: University of Oxford; 2017. TrueColours: Real Time Data Collection in Patients With
Ulcerative Colitis. URL: https://pdfs.semanticscholar.org/32cc/acc043c3060f8c404efdaaaa718158d11f45.pdf [accessed
2019-11-20]
44. Probert F, Walsh A, Jagielowicz M, Yeo T, Claridge T, Simmons A, et al. Plasma nuclear magnetic resonance metabolomics
discriminates between high and low endoscopic activity and predicts progression in a prospective cohort of patients with
ulcerative colitis. J Crohns Colitis 2018 Nov 15;12(11):1326-1337 [FREE Full text] [doi: 10.1093/ecco-jcc/jjy101] [Medline:
30016408]
45. Walsh A, Matini L, Hinds C, Sexton V, Brain O, Keshav S, et al. Real-time data monitoring for ulcerative colitis: patient
perception and qualitative analysis. Intest Res 2019 Jul;17(3):365-374 [FREE Full text] [doi: 10.5217/ir.2018.00173]
[Medline: 31146510]
46. Oxford Health NHS Foundation Trust. Treatment. URL: https://www.oxfordhealth.nhs.uk/ketamine-service/treatment/
[accessed 2019-11-25]
47. Research 2 Guidance. 2017. mHealth Economics 2017 – Current Status and Future Trends in Mobile Health. URL: https:/
/research2guidance.com/product/mhealth-economics-2017-current-status-and-future-trends-in-mobile-health/ [accessed
2019-11-20]
48. Payne HE, Lister C, West JH, Bernhardt JM. Behavioral functionality of mobile apps in health interventions: a systematic
review of the literature. JMIR Mhealth Uhealth 2015 Feb 26;3(1):e20 [FREE Full text] [doi: 10.2196/mhealth.3335]
[Medline: 25803705]
J Med Internet Res 2020 | vol. 22 | iss. 1 | e15188 | p. 9https://www.jmir.org/2020/1/e15188 (page number not for citation purposes)
Goodday et alJOURNAL OF MEDICAL INTERNET RESEARCH
XSL
•
FO
RenderX
49. Peake JM, Kerr G, Sullivan JP. A critical review of consumer wearables, mobile applications, and equipment for providing
biofeedback, monitoring stress, and sleep in physically active populations. Front Physiol 2018;9:743 [FREE Full text] [doi:
10.3389/fphys.2018.00743] [Medline: 30002629]
50. Mclean V. PRWeb. 2011 Apr 25. Motivating Patients to Use Smartphone Health Apps. URL: http://www.prweb.com/
releases/2011/04/prweb5268884.htm [accessed 2019-11-20]
51. Baker TB, Gustafson DH, Shah D. How can research keep up with eHealth? Ten strategies for increasing the timeliness
and usefulness of eHealth research. J Med Internet Res 2014 Feb 19;16(2):e36 [FREE Full text] [doi: 10.2196/jmir.2925]
[Medline: 24554442]
52. Bauer M, Grof P, Gyulai L, Rasgon N, Glenn T, Whybrow PC. Using technology to improve longitudinal studies:
self-reporting with ChronoRecord in bipolar disorder. Bipolar Disord 2004 Feb;6(1):67-74. [doi:
10.1046/j.1399-5618.2003.00085.x] [Medline: 14996143]
53. Faurholt-Jepsen M, Vinberg M, Christensen EM, Frost M, Bardram J, Kessing LV. Daily electronic self-monitoring of
subjective and objective symptoms in bipolar disorder--the MONARCA trial protocol (MONitoring, treAtment and
pRediCtion of bipolAr disorder episodes): a randomised controlled single-blind trial. BMJ Open 2013;3(7):pii: e003353
[FREE Full text] [doi: 10.1136/bmjopen-2013-003353] [Medline: 23883891]
54. PatientsLikeMe. URL: https://www.patientslikeme.com/ [accessed 2019-11-20]
55. Bauer M, Glenn T, Monteith S, Bauer R, Whybrow PC, Geddes J. Ethical perspectives on recommending digital technology
for patients with mental illness. Int J Bipolar Disord 2017 Dec;5(1):6 [FREE Full text] [doi: 10.1186/s40345-017-0073-9]
[Medline: 28155206]
56. Torous J, Onnela JP, Keshavan M. New dimensions and new tools to realize the potential of RDoC: digital phenotyping
via smartphones and connected devices. Transl Psychiatry 2017 Mar 7;7(3):e1053 [FREE Full text] [doi: 10.1038/tp.2017.25]
[Medline: 28267146]
57. Nipp RD, El-Jawahri A, Ruddy M, Fuh C, Temel B, D'Arpino SM, et al. Pilot randomized trial of an electronic symptom
monitoring intervention for hospitalized patients with cancer. Ann Oncol 2019 Feb 1;30(2):274-280. [doi:
10.1093/annonc/mdy488] [Medline: 30395144]
58. Basch E, Deal AM, Dueck AC, Scher HI, Kris MG, Hudis C, et al. Overall survival results of a trial assessing patient-reported
outcomes for symptom monitoring during routine cancer treatment. J Am Med Assoc 2017 Jul 11;318(2):197-198 [FREE
Full text] [doi: 10.1001/jama.2017.7156] [Medline: 28586821]
Abbreviations
AMoSS: Automated Monitoring of Symptom Severity Study
BD: bipolar disorder
BDRN: BD Research Network
BIRSH: Brief Interventions for Self-Harm
ConBrio: Collaborative Network for Bipolar Research to Improve Outcomes
FIMM: Facilitated Integrated Mood Management
FWwTC: Feeling Well with True Colours
IBD: inflammatory bowel disease
NIHR: National Institute for Health Research
OxCaMS: Oxford Study of Calcium Channel Antagonism, Cognition, Mood instability and Sleep
OxLith: Oxford Lithium Trial
PROM: patient-reported outcome measure
RCT: randomized controlled trial
Edited by G Eysenbach; submitted 15.07.19; peer-reviewed by A Tsolaki, D Gustafson; comments to author 14.09.19; revised version
received 25.09.19; accepted 22.10.19; published 15.01.20
Please cite as:
Goodday SM, Atkinson L, Goodwin G, Saunders K, South M, Mackay C, Denis M, Hinds C, Attenburrow MJ, Davies J, Welch J,
Stevens W, Mansfield K, Suvilehto J, Geddes J
The True Colours Remote Symptom Monitoring System: A Decade of Evolution
J Med Internet Res 2020;22(1):e15188
URL: https://www.jmir.org/2020/1/e15188
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©Sarah M Goodday, Lauren Atkinson, Guy Goodwin, Kate Saunders, Matthew South, Clare Mackay, Mike Denis, Chris Hinds,
Mary-Jane Attenburrow, Jim Davies, James Welch, William Stevens, Karen Mansfield, Juulia Suvilehto, John Geddes. Originally
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