Mobile mental health: Review of the emerging field and proof of concept study

Abstract

The ubiquitous nature of mobile phones and their increasing functionality make them an ideal medium for the delivery of large-scale public health information and interventions. While mobile phones have been used to this end in behavioural and physical health settings, their role in monitoring and managing mental health is in its infancy.
The purpose of this paper is (1) to provide an overview of the field of mobile mental health and (2) by way of illustration, describe an initial proof of concept study carried out to assess the potential utility and effectiveness of a newly developed mobile phone and web-based program in the management of mild-to-moderate stress, anxiety and depression.
Over 6 weeks, participants were given access to "myCompass": an interactive self-help program, which includes real-time self-monitoring with short message service prompts and brief online modules grounded in cognitive behavioural therapy.
Preliminary analyses found that participants' symptoms of stress, anxiety, depression and overall psychological distress were significantly reduced after using myCompass. Improvements were also found in functional impairment and self-efficacy.
These preliminary results support the feasibility of implementing mobile phone-based interventions with the potential of improving psychological wellbeing.

Full text

Mobile mental health: Review of the emerging field and
proof of concept study
VIRGINIA HARRISON
1,2
, JUDITH PROUDFOOT
1,2
, PANG PING WEE
1,2
,
GORDON PARKER
1,2
, DUSAN HADZI PAVLOVIC
1,2
&
VIJAYA MANICAVASAGAR
1,2
1
School of Psychiatry, University of New South Wales, Sydney, Australia and
2
Black Dog Institute,
Sydney, Australia
Abstract
Background: The ubiquitous nature of mobile phones and their increasing functionality make them an
ideal medium for the delivery of large-scale public health information and interventions. While mobile
phones have been used to this end in behavioural and physical health settings, their role in monitoring
and managing mental health is in its infancy.
Aims: The purpose of this paper is (1) to provide an overview of the field of mobile mental health and (2)
by way of illustration, describe an initial proof of concept study carried out to assess the potential utility
and effectiveness of a newly developed mobile phone and web-based program in the management of
mild-to-moderate stress, anxiety and depression.
Methods: Over 6 weeks, participants were given access to “myCompass”: an interactive self-help
program, which includes real-time self-monitoring with short message service prompts and brief
online modules grounded in cognitive behavioural therapy.
Results: Preliminary analyses found that participants’symptoms of stress, anxiety, depression and overall
psychological distress were significantly reduced after using myCompass. Improvements were also
found in functional impairment and self-efficacy.
Conclusions: These preliminary results support the feasibility of implementing mobile phone-based
interventions with the potential of improving psychological wellbeing.
Keywords: mobile phones,monitoring,self-help,depression,anxiety,stress,mobile mental health,
internet intervention
Background
Mood and anxiety-related problems present a global public-health concern. They are both
highly prevalent and disabling (Andrews et al., 2001; Andrews & Titov, 2007), yet only a
minority of people seek professional help for these conditions (Australian Bureau of Statistics
[ABS], 2007; Wang et al., 2002). One solution is to enhance public access to information,
self-help strategies and treatment services through different channels. In addition to
Correspondence: Judith Proudfoot, School of Psychiatry, University of New South Wales, Sydney, and Black Dog Institute, Hospital
Road, Sydney, NSW 2031, Australia. Tel: +61 (2) 9382 3767. Fax: +61 (2) 9382 8207. E-mail: j.proudfoot@unsw.edu.au
Journal of Mental Health, December 2011; 20(6): 509–524
© 2011 Informa UK, Ltd.
ISSN: 0963-8237 print / ISSN 1360-0567 online
DOI: 10.3109/09638237.2011.608746
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face-to-face help, there is an increasing trend to utilize electronic media for evidence-based
prevention, early intervention, treatment and self-management programs.
Over the last decade, a significant number of computer-based monitoring, psycho-
education and therapy programs have been developed for mental health conditions including
depression, anxiety disorders, stress, bipolar disorder, substance use disorders and eating
disorders. These programs have good take-up (Christensen et al., 2002) and attitudes
towards their use are positive (Graham et al., 2000). Additionally, outcome data from
randomized controlled trials (RCTs) and meta-analyses have demonstrated that eMental
Health programs are clinically efficacious and cost-effective (Barak et al., 2008; Griffiths &
Christensen, 2006), and in the case of depression and anxiety disorders, they produce com-
parable effect sizes to face-to-face treatment (Andersson & Cuijpers, 2009; Cuijpers et al.,
2009).
However, computer-based interventions also have limitations and challenges. They rely
on users being able to access a computer at scheduled times (for monitoring or homework
completion, for example), which can sometimes prove difficult or inconvenient. In addition,
internet access is often restricted and unreliable in rural or remote areas. Mobile phones offer
an innovative solution.
The purposes of this paper are twofold. First, we provide an overview of the field of mobile
mental health, that is, the use of mobile devices (most commonly mobile phones) in mental
health care delivery. Second, we describe a proof of concept study carried out to assess the
potential utility and effectiveness of a newly developed mobile phone and web-based
program (myCompass) for the management of mild-to-moderate stress, anxiety and
depression.
Overview of mobile mental health
Suitability of mobile phones for mental health monitoring and management
Mobile phone ownership has increased exponentially in the last two decades. In 2010, global
mobile phone subscriptions reached 5 billion, and this number is predicted to climb to 50
billion by 2020 (Ericsson, 2010). Unlike the dissemination of other technologies, the
rapid uptake of mobile phones is not restricted to developed countries (World Health Organ-
isation, 2011). Their cost has dropped dramatically and functionality continues to expand.
These factors make mobile phones ideal for the delivery of mental health information, strat-
egies and support. Yet, despite their use for a multitude of everyday purposes (especially
since mobile “apps”exploded into the global marketplace), the appropriation of mobile
phones for monitoring and managing mental health is in its infancy. Initial work looks posi-
tive, however. Proudfoot and colleagues (2010) investigated attitudes of the Australian
general public towards using mobile phones for mental health monitoring and self-
management. The majority of survey respondents (399/525, 76%) and focus group
participants (33/47, 70%) indicated that they would be interested in using their mobile
phones for these purposes. In addition, a recent pilot study (n= 18) of a youth-friendly
mobile phone program to monitor mood, stress and coping behaviours reported that over
a 7-day monitoring period, 76% (382/504) of entries were completed, suggesting a high
level of engagement in this delivery method among adolescents (Reid et al., 2009a).
Mobile phones are particularly suited to mental health care delivery, as their ownership is
not restricted by socio-economic or demographic status, and they are the preferred means of
communication among young people, the age group most commonly affected by mental
health issues and unlikely to seek treatment (ABS, 2007; Oliver et al., 2005). In addition,
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mobile phones have extensive global networks and can be used (almost) anywhere in real
time. This allows users to access electronic psychotherapeutic strategies and support where
and when they need them; an option usually not possible in face-to-face therapy.
As mobile phones are largely location independent, usually carried with the owner and
turned on, they facilitate ecological momentary assessment: the collection of self-monitoring
information as people go about their everyday activities. Self-monitoring not only acts as an
intervention in itself (producing improvements in mood and behaviour), but it also improves
individuals’compliance with treatment programs (Proudfoot & Nicholas, 2010; Thiele et al.,
2002). Historically, paper diaries have primarily been used for monitoring. However, this
method relies on people remembering to carry their diaries with them and fill them out,
which can result in poor adherence rates, last-minute, retrospective completion and
memory errors (Stone et al., 2002). In contrast, adherence and compliance rates are signifi-
cantly improved when patients use an electronic diary for their monitoring activities (Stone
et al., 2002, 2003). Mobile phones allow real-time monitoring which can be accompanied by
the collection of situational information to aid identification of triggers and patterns in
moods or behaviours. Short message service (SMS) reminders can also be sent as memory
prompts.
In addition, mobile phones can be used to encourage the completion of therapy-related
homework tasks. For example, when planning their behavioural experiments or thought
recording activities, users can program their mobile phones to send them keywords and/or
reminders at scheduled times. With the constant upgrading of mobile phone handsets and
the reduction in data plan costs, mobile phones have also become a viable option for the
delivery of simple self-management strategies, information and tips.
The efficacy of mobile phone monitoring systems is well established in the area of physical
health, particularly as an adjunct to traditional forms of patient care. Mobile monitoring has
produced positive health outcomes in chronic conditions such as diabetes (Ferrer-Roca
et al., 2004; Franklin et al., 2003; Kwon et al., 2004; Rami et al., 2006), asthma (Anhoj &
Moldrup, 2004; Ostojic et al., 2005), migraine (Sorbi, 2009) and HIV (Puccio et al.,
2006) both by increasing treatment compliance and wellbeing. However, their use in the
treatment of hypertension (Logan et al., 2007; Márquez et al., 2004) has produced inconsist-
ent results in terms of health outcomes.
Mobile phones have also been used effectively in lifestyle interventions, such as for the
reduction of alcohol consumption (Collins et al., 2003), smoking (Bramley et al., 2005;
Obermayer et al., 2004; Rodgers et al., 2005; Whittaker et al., 2008) and gambling (Gee
et al., 2005), as well as to enhance physical activity (Hurling et al., 2007) and control
weight (Bauer et al., 2010; Joo & Kim, 2007).
Despite the extent of mobile health work in the physical and lifestyle health domains, there
has been little activity exploring the use of mobile phones in mental health. This is surprising
given how easily self-monitoring and manualized therapies (e.g. cognitive behavioural
therapy (CBT)) lend themselves to such devices.
Review of current mobile mental health programs
Mobile mental health programs were identified by searching PubMed (incorporating
MEDLINE), EMBASE, PsycINFO and the web of science using combinations of the
search terms presented in Figure 1. Reference lists of identified studies were then examined
and authors of ongoing studies were contacted. No restrictions were placed on publication
date or language. Only six mobile monitoring programs were identified in the mental
health field.
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(a) Building on the success of their mobile smoking reduction programs (“STOMP”;
Bramley et al., 2005; Whittaker et al., 2008) the Clinical Trials Research Unit at the
University of Auckland have developed a mobile phone program (“MEMO”; Stasiak
et al., 2010) to promote psychological resilience and to decrease depression in adoles-
cents. The content of the program is based on positive psychology and CBT and is
delivered using video messages, text messages and animations, as well as a mobile
website for consolidating what they have learnt. An RCT is currently underway to
assess the effectiveness of the program.
(b) Reid and colleagues in Australia have developed a mobile phone program to assist clin-
icians to detect and manage youth mental health problems (Reid et al., 2009a).
Doctors prescribe the “Mobiletype”program to their patients who track their mood,
coping strategies, alcohol and cannabis use, eating patterns and general lifestyle
factors on a regular basis for a few weeks. The information is uploaded to a secure
website where a tracking report can be viewed by the doctor. An initial pilot study
revealed that doctors (n= 2) found the reports helped them gain insight into the
mental wellbeing of their patients (Reid et al., 2009b), enabling them to better treat
their patients’mental health problems. In addition, pre–post analysis of the patients’
mood showed that depression and stress were reduced after using the program
(n= 22). An RCT is currently underway testing the efficacy of the program on
young peoples’mental health and pathways to care.
(c) An “ambient intelligent system”has been developed to support people with depression
through a mobile and web-based behavioural activation intervention. This program
Figure 1. Search terms for literature review. ∗Indicates a truncated search term.
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uses daily mobile monitoring to encourage participants to form a link between their
mood and the number of pleasant activities in which they take part. The system ana-
lyses their behaviour and sends them reminders, advice and feedback with the aim
of enhancing mood (Both et al., 2009). A clinical trial assessing the effectiveness
and acceptability of this program is currently underway at Vrije Universiteit
Amsterdam.
(d) Preliminary outcome data have been published by Morris et al. (2010) concerning
their program Intel “Mobile Heart Health”Therapy. The program involves regular
monitoring of self-reported mood (using a touch screen mood map) and via physio-
logical sensors that detect heart rate variability. The aim of the program is to reduce
stress and the related risk of cardio-vascular disease. If users appear stressed on the
measured dimensions, they receive CBT or positive psychology-based feedback and
exercises. Contextual information is concurrently collected to promote emotional
self-awareness, and to aid the development of coping strategies for stress management
(Morris & Guilak, 2009). A recent case study exploration of the program (without the
use of the physiological sensors) found it to be well accepted by participants and effec-
tive in the reduction of anger, anxiety and sadness (Morris et al., 2010). The qualitative
data from the five case studies demonstrated the potential of mobile phone technology
to improve mental health.
(e) An SMS intervention developed by Bauer and colleagues (2003) to support patients
after inpatient CBT for bulimia nervosa requires patients to submit weekly information
about their bulimic symptoms via SMS. The program analyses the received data and,
using an algorithm, automatically selects relevant feedback from a large pre-written
text pool, which is sent back to the user by SMS. A later version of this program in-
creased the frequency of symptom monitoring and feedback to a daily basis (Shapiro
et al., 2009). Bauer et al. (2006) and Shapiro et al. (2009) found the 6-month pro-
grams to be well accepted by patients, and potentially effective in the reduction of
relapse rates. In addition, Shapiro et al. (2009) reported a high self-monitoring adher-
ence rate (87%) and significantly reduced bulimic symptoms following program use.
However, a feasibility study of the initial program found high dropout and low self-
monitoring rates (Robinson et al., 2006) as well as no significant change in symptoms.
These conflicting findings, together with their uncontrolled designs and small sample
sizes, limit the conclusions that can be drawn. It is of note, however, that all were proof-
of-concept or pilot studies. A large-scale RCT is currently underway at the University
of Heidelberg.
(f ) The sixth program, “myCompass”is a self-monitoring and a self-management
program for people with mild-to-moderate stress, anxiety and depression, which is
delivered via the internet on users’own mobile phones and computers. The
program is described in the following section as part of a proof of concept study that
has been undertaken to assess its potential utility and effectiveness. The second aim
of this paper is to report the findings of the study, as outlined below. It was
hypothesized that symptoms of stress, anxiety and/or depression and functional impair-
ment would be reduced after using myCompass, while mental health self-efficacy was
expected to improve.
Proof of concept study
Description of the intervention. Developed at the Black Dog Institute in Australia, the
myCompass program is a self-help tool offering a number of interactive and tailored
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functions. Users can track their moods, symptoms and behaviours (e.g. depression, anxiety,
irritability, physical activity, concentration), schedule SMS tracking reminders, receive
graphical feedback about their monitoring which is presented alongside meaningful, contex-
tual information, learn about mental health conditions and gain helpful tips and self-help
strategies. myCompass also offers a number of CBT-based self-management modules.
Users can choose which modules they wish to use, or the program will recommend
modules that are most relevant and beneficial for a person, based on his/her answers in the
set-up questionnaire. The modules are interactive, three sessions in length, each session of
approximately 10 min duration accessed via the internet on users’computers. There are
“homework tasks”between the sessions.
myCompass is designed to suit a wide range of people from different age groups and tech-
nological literacy. The program works on virtually all internet-enabled handsets, thereby al-
lowing users to access the program on their own phone, rather than having to carry a second
“mental health”phone with them. Full usage of the myCompass program takes a fraction of
the monthly data allowance on most mobile phone plans. Particular care is taken in the my-
Compass program to protect users’privacy by having a password-protected log-on, by ensur-
ing that data are not stored on the phone, by transferring data via the internet using secure
sockets layer protocols (which encrypts transmitted data rendering it unreadable to anyone
except the intended recipient) and by storing the data in secure servers.
Method
A combined quantitative and qualitative method consisting of online questionnaires and tel-
ephone interviews was used to investigate participants’views of the program’s utility and
effectiveness.
Participants
Participants were recruited via the Black Dog Institute (BDI) website, as well as through ad-
vertisements disseminated through the online BDI research volunteer register. Eligibility cri-
teria are outlined in Figure 2.
Measures
Participants completed questionnaires before and after using the myCompass program. At
baseline, demographic information was collected to assess participant characteristics. In
Figure 2. Inclusion and exclusion criteria.
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addition, information about participants’frequency of mobile phone and computer use was
collected to assess familiarity and engagement with these technologies. Also included in the
baseline questionnaire was the 10-Item Personality Inventory (Gosling et al., 2003). This
measure contains five two-item sub-scales for each personality dimension (extraversion,
agreeableness, conscientiousness, emotional stability and openness). Each item has a 7-
point Likert-scale producing a possible score range of 2–14, with higher scores indicating
higher levels of each trait.
Additional measures administered in both the pre- and post-intervention questionnaires
included:
(1) The depression, anxiety and stress scales (DASS-21; Lovibond & Lovibond, 1995),
which comprises three scales. Each scale contains seven symptom-based items
which participants respond to using a 0-3 Likert-scale. Scores on each item are
summed and then doubled for each scale, producing a possible range of 0–42, with
higher scores indicating more severe symptoms.
(2) The work and social adjustment scale (WSAS; Mundt et al., 2002). Here participants
indicate their subjective impairment across five different domains (work, home man-
agement, social life, private leisure and relationships) on 0-8 point Likert-scales. Item
scores are then summed, producing a possible range of 0–40, with higher scores indi-
cating greater functional impairment.
(3) The mental health self-efficacy scale (MHSES), a new scale developed by the authors
using Bandura’s guide for constructing self-efficacy scales (Bandura, 2006). Self-
efficacy expectancies are a significant predictor of health-related outcomes
(Bandura, 1982), therefore the construct is theoretically relevant for this research.
Yet while there are several self-efficacy scales relating to physical health and lifestyle
improvements, our review of the literature failed to locate a scale to measure self-effi-
cacy for common mental health conditions. Designed to measure participants’confi-
dence in managing their own mental health, our scale comprises six items, each with a
Likert response scale from 1 to 10, where higher responses indicate higher levels of
perceived self-efficacy and confidence in managing their mental health issues. An
example of a scale item is “In the next month, how confident are you that you will
be able to effectively manage any stress, anxiety or depression that you experience?”
Design
A one-group pretest–posttest design was used with one independent variable: time ( pre- and
post-intervention). The dependent variables were depression, anxiety and stress symptoma-
tology, functional impairment and mental health self-efficacy. In addition, personality ratings
and frequency of technology usage were included as predictor variables.
Procedure
Participants completed the baseline questionnaire and were then given access to the
myCompass program. They were asked to use myCompass over a 6-week period. In week
7, after using the program, participants completed the post-intervention questionnaire.
Both questionnaires were delivered online.
All participants who took part in the study were invited to take part in a follow-up tele-
phone interview to gather further information about their experiences with the program.
Given that regular engagement with the program is needed to maximize the utility and
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effectiveness of myCompass (and indeed any self-monitoring program), reasons for take-up
of the program and for adherence and non-adherence (as relevant) were examined in the
interviews.
A standard “sampling to saturation”recruitment method resulted in a total of 16 inter-
views. The interviews were audio taped and later transcribed to allow for identification of
emergent themes.
The study was approved by Human Research Ethics Committee of the University of New
South Wales (HREC 10019).
Analyses
Data from each component of the study were analysed separately and then synthesized. For
the questionnaire data, an intention-to-treat analysis was carried out in order to deal with
missing data. Using a last-observation-carried-forward (LOCF) method, missing post-
intervention data were replaced with the last observed score on that measure.
Total DASS scores were computed in order to represent overall psychological distress.
Related samples t-tests were carried out to identify changes in the stress sub-scale, WSAS
and MHSES scores. The nonparametric Wilcoxon test was used where the assumption of
normality was violated on other outcome variables.
Scores on the five personality dimensions and frequency of technology usage were entered
into a step-wise multiple regression to determine whether they predicted intervention out-
comes (as measured by pre–post difference scores on each of the dependent variables).
These change scores were also entered into a binary logistic regression to determine
whether outcome was related to adherence.
For the interview data, thematic analysis (Braun & Clark, 2006) was used to identify
salient themes and ideas.
Results
In total, 49 individuals met inclusion criteria of whom 47 completed the baseline
questionnaire. Three withdrew consent before or during the study. Included in the
final analyses were 44 participants (70.5% female) with a mean age of 38.2 (SD
= 12.6) years. As Figure 3 illustrates, 63.63% of those participants completed both
the baseline and post-questionnaires (n= 28; 75% female; mean age = 37.6 years;
SD = 14.0 years).
Questionnaires
Symptomatology
As illustrated in Figure 4, all symptoms of stress, anxiety and depression appeared to decrease
after using the myCompass program. Results of a related samples t-test revealed that this
pattern was significant for symptoms of stress (t(43) = 3.10, p< 0.01, d= 0.51). Wilcoxon
tests confirmed that this was also the case for anxiety and depression (z=−2.80, p< 0.01,
r= 0.32; z=−3.26, p= 0.001, r= 0.54, respectively). A significant reduction was also
found in total DASS scores (z=−3.30, p= 0.001, r= 0.57).
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Functional impairment and self-efficacy
Results of the related samples t-test on WSAS scores illustrated that participants reported
being less impaired after using myCompass than they were before (t(27) = 2.42,
p< 0.05, d= 0.32). In addition, after the 6-week intervention period, participants also
reported significantly increased self-efficacy (t(43) = −2.80 p< 0.01, d=−0.37).
Predictors
Results of the step-wise multiple regression identified one significant finding. The personal-
ity dimension of emotional stability was found to be a significant predictor of intervention
outcome in terms of anxiety symptoms (t(42) = −2.29, p< 0.05; β=−0.33), with
lower scores predicting a greater change.
Engagement
In all, 63.6% (28/44) of participants returned the post-intervention questionnaire, and of
those, 81.5% (22/27) reported using at least one myCompass module and 92.6% (25/27)
reported tracking their moods, feelings or behaviours. However, when the frequency of
use was investigated, 55.6% (15/27) of participants reported accessing myCompass on at
least a weekly basis to use the CBT modules and 66.6% (18/27) reported accessing
myCompass on at least a weekly basis for monitoring (Figure 5). Using a very stringent defi-
nition of participant adherence (active use of at least one module, and monitoring mood and
behaviours for least 3–4 days a week for 6 weeks), we found that 40.7% (11/27) of
Figure 3. Participant flow diagram.
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participants who completed the post-questionnaire fulfilled this criterion. A binary logistic
regression (n= 27) yielded no significant results, suggesting that adherence was not
related to outcome.
Interviews
Reasons for using myCompass: Key themes identified. Many interviewees reported that one of
the main reasons for using (and continuing to use) myCompass was its accessibility, conven-
ience and being able to use the program anywhere and at any time.
I find that it’s a lot easier for me to use the program when I’m commuting or when I have
some spare time, but if I had to log in to the applications and sit in front of the computer, I
don’t think I would use the program. It would not make it quite as personal and intimate as
having it on your phone. (Male, aged 27).
Figure 4. Means scores on dependent variables before and after using myCompass (error bars = ± 1 standard
error).
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Users also reported that they found the content of the myCompass program helpful, which
encouraged them to continue using the program.
I really didn’t want to be medicated for my depression. I wanted to manage it through
exercise, good sleep, going to a psychologist and something like the myCompass
program which is a nice rounding to those factors. (Female, aged 32)
Some participants highlighted that the inter-relationship between the myCompass
functions was particularly helpful. For example, being able to observe their mood changes
graphically, correlate this pattern with their online diary entries and then use the self-help
modules to manage these issues.
Reasons for discontinuation myCompass: Key themes identified. The main reason given for dis-
continuation was insufficient instruction about how to use the different functions of the
program. Specifically, a number of interviewees reported being unclear about how to set
up the tracking function, which modules to use and where to find the feedback graphs.
They suggested that had the instructions been clearer, they would have engaged more fully
with the program.
A number of users also reported experiencing technological issues, while others, especially
the older users, struggled to access myCompass on their mobile phones due to their inexperi-
ence with mobile internet browsing.
I only use my mobile phone for emergencies and occasionally to send a SMS. I am not like
most young people who use their mobile phone for lots of things. For me it’s just in my bag
in case I have an emergency. (Female, aged 59)
Others reported the inconvenience of patchy mobile internet access and found it hard to
use the program as often as they wanted.
Figure 5. Frequency of participants’use of the myCompass program.
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I was travelling at that time, so I found that I wasn’t always able to connect to the internet to
be able to use it. (Male, 27-year-old)
Some users also cited lack of motivation to use or complete the program, although whether
this was associated with their mental health problems, personality characteristics or other
reasons is unclear. For example, some reported that myCompass was not prioritized when
they had other things to do.
…that’s just the thing about modern life more than anything. It’s not the design of the
program. It’s that life gets very busy. (Female, aged 59)
Improving myCompass: Key themes identified. When participants were asked to suggest poss-
ible future enhancements to the “myCompass program”, especially to improve usage, the
main themes that arose were more personalized instructions based on user answers and
better “signposting”within the program. In particular, the inclusion of default reminders
to monitor their mood or complete their homework and more interactive information to
direct their attention to the most relevant components of the program were identified as
important features to motivate use.
…to have an SMS service that gives feedback on my feelings and directs me to a module for
me to work through on the spot…(Female, 54-year-old)
…It would be really good to have it connected to your pulse rate and breathing rate, so that
you can get biofeedback about how you are going with your breathing. If you were breath-
ing in a shallow way, you’d get a reminder saying “Remember to do diaphragm breathing”.
(Female, aged 59)
Discussion
In line with research on the use of mobile phones for physical and behavioural health, the
results of this pilot study indicate that mobile phones may be suitable and effective for facil-
itating monitoring and self-management of psychological problems. Specifically, partici-
pants’symptoms of stress, anxiety, depression and overall psychological distress were
found to be significantly reduced after the use of myCompass, and improvements were
found in functional impairment and perceived self-efficacy for managing one’sown
mental health. As such, our findings appear to support those of Morris et al. (2010) who
reported reduced problems with mood following use of their mobile monitoring program.
However, our conclusions can only be tentative due to the limitations inherent in the
study design (i.e. the lack of a control group which disallows any causality claim) and the
imputation method used (i.e. LOCF biases). In contrast to the work of Reid et al.
(2009a), who found that 76% of entries were completed over a 7-day monitoring period
by adolescents using a mood mobile monitoring program, we had a lower usage rate
(41%) over our 6-week period. The difference in the response rate may be due to the
longer monitoring period or the wider age range in our sample. Nevertheless, the level of
adherence found in this study compares favourably to other open access online mental
health programs, where drop-out rates of 83% (Melville et al., 2010) and 90% (Farvolden
et al., 2005) have been reported. Open access self-help programs not accompanied by
contact from a therapist or support person are generally associated with higher drop-out
rates than “guided”or “supported”programs (Cavanagh, 2010).
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However, given the prolific use of mobile phones among young people for text-based com-
munication (be it SMS or internet-based mobile sites like those used for social networking),
it may be a smaller “leap”for this age group to use their phones on a regular basis for mood
monitoring. While Proudfoot et al. (2010) found that adults reported they were prepared to
use a mobile-based program for managing mental health issues, it is possible that in practice,
take-up rates may not be as high, or strong initial usage rates may diminish after a certain
period. Lack of familiarity with using mobile phones for functions other than communication
by some adults may necessitate more instruction and active encouragement to use mobile
mental health programs to their fullest. As a result of our findings, additional features have
been incorporated in a recent upgrade of the myCompass program in order to maximize
user engagement and program utility.
Despite the low adherence rate in this study, it is interesting to note that intention-to-treat
analyses suggest that the program may be associated with reduced symptomatology and im-
pairment. This may reflect the designed benefit of the program (where discontinuation may
be due to positive reasons, such as improvement), but other non-causal explanations such as
regression to the mean and spontaneous improvement are also plausible.
In developing a mobile mental health intervention, clinicians and program facilitators
need to consider a number of important issues (Boschen & Casey, 2008). Privacy and secur-
ity are paramount and drive all other considerations, a message also strongly communicated
by members of the public (Proudfoot et al., 2010). Compatibility may also be an issue; as
mobile phone handsets are not all equal in their functionality, a mobile health program
that works on one phone may not work on another. Data charges and SMS costs can also
vary considerably across different service plans, which may render some mobile health
programs unaffordable. In addition, mobile coverage can be variable within and between
networks, resulting in intermittent coverage (an issue the users in this study found
frustrating). With the expansion of mobile networks, decreases in usage costs and increases
in technology, it is possible that many of these issues will become redundant (except in the
case of privacy and security).
Limitations
As this was a preliminary proof of concept study, there were a number of limitations. First,
the small-sample size and recruitment of participants though the Black Dog Institute
website and volunteer register limits the degree to which these findings may be representative
or generalizable. A second limitation was the uncontrolled pre–post design which does not
take into account factors such as spontaneous remission, regression to the mean and the
placebo and Hawthorne effects. Third, the use of LOCF imputations may have resulted in
the production of biased treatment effects. In order to overcome such issues, and to more
accurately assess the efficacy of the myCompass program, a large-scale RCT is currently
being conducted.
Conclusion
Initial research in the area of mobile mental health suggests that mobile phones may be well
accepted by the public as tools to manage mental health issues, and existing mobile mental
health programs also show promising results as far as effectiveness is concerned. However, it
is possible that there may be an age-related difference in take-up, with young people more
likely to engage. Mobile mental health programs for older age groups may be effective as
our results seem to indicate, but additional focus is needed when developing such programs
Mobile mental health 521
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to ensure they are intuitive, clearly signposted and easy to use. Because of their low cost and
widespread use, mobile phones offer a means of providing access to evidence-based infor-
mation, strategies and support that is yet to be fully realized.
Declaration of Interest: The authors report no conflicts of interest. The authors alone are
responsible for the content and writing of the paper. The project was funded by the
Department of Health and Ageing, and the National Health and Medical Research
Council (Programme Grant Number: 510135).
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