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


Abstract and Figures

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.
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Mobile mental health: Review of the emerging field and
proof of concept study
School of Psychiatry, University of New South Wales, Sydney, Australia and
Black Dog Institute,
Sydney, Australia
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 participantssymptoms 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
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:
Journal of Mental Health, December 2011; 20(6): 509524
© 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.,
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
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 appsexploded 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
individualscompliance 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
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 Mobiletypeprogram 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 patientsmental health problems. In addition, prepost 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 peoplesmental health and pathways to care.
(c) An ambient intelligent systemhas 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
(d) Preliminary outcome data have been published by Morris et al. (2010) concerning
their program Intel Mobile Heart HealthTherapy. 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, myCompassis 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 usersown 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 userscomputers. There are
homework tasksbetween 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 healthphone 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 usersprivacy 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.
A combined quantitative and qualitative method consisting of online questionnaires and tel-
ephone interviews was used to investigate participantsviews of the programs utility and
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.
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 participantsfrequency 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 214, with higher scores indicating
higher levels of each trait.
Additional measures administered in both the pre- and post-intervention questionnaires
(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 042, 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 040, with higher scores indi-
cating greater functional impairment.
(3) The mental health self-efficacy scale (MHSES), a new scale developed by the authors
using Banduras 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 participantsconfi-
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?
A one-group pretestposttest 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.
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
A standard sampling to saturationrecruitment 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).
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 prepost 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.
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).
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).
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.
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 34 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.
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 its a lot easier for me to use the program when Im 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
dont 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
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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 didnt 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 its 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 participantsuse of the myCompass program.
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I was travelling at that time, so I found that I wasnt 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.
thats just the thing about modern life more than anything. Its not the design of the
program. Its 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 signpostingwithin 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, youd get a reminder saying Remember to do diaphragm breathing.
(Female, aged 59)
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-
pantssymptoms 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 onesown
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 guidedor supportedprograms (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 leapfor 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).
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 prepost 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.
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).
Andersson, C., & Cuijpers, P. (2009). Internet-based and other computerised psychological treatments for adult
depression: A meta-analytic review. Cognitive Behavioral Therapy,38(4), 196205.
Andrews, G., Henderson, S., & Hall, W. (2001). Prevalence, comorbidity, disability and service utilisation. British
Journal of Psychiatry,178, 145153.
Andrews, G., & Titov, N. (2007). Depression is very disabling. Lancet,370, 808809.
Anhoj, J., & Moldrup, C. (2004). Feasibility of collecting diary data from asthma patients through mobile phones
and SMS (short message service): Response rate analysis and focus group evaluation from a pilot study.
Journal of Medical Internet Research,6(4), e42.
Australian Bureau of Statistics. (2007). National Survey of Mental Health and Wellbeing: Summary of Results. Canberra:
ABS, Cat. No. 4326.0.
Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist,37, 122147.
Bandura, A. (2006). Guide for constructing self-efficacy scales. In F. Pajares, & T. Urdan (Eds.), Self-efficacy Beliefs
of Adolescents (Vol. 5, pp. 307337). Greenwich, CT: Information Age Publishing.
Barak, A., Hen, L., Boniel-Nissim, M., & Shapira, N. (2008). A comprehensive review and a meta-analysis of the
effectiveness of internet-based psychotherapeutic interventions. Journal of Technology in Human Services,26,
Bauer, S., Hagel, J., Okon, E., Meermann, R., & Kordy, H. (2006). Experiences with the use of short message
service in the post-hospitalization follow-up care of patients with bulimia nervosa. Psychodynamische Psychother-
apie,3, 127136.
Bauer, S., de Niet, J., Timman, R., & Kordy, H. (2010). Enhancement of care through self-monitoring and tailored
feedback via text messaging and their use in the treatment of childhood overweight. Patient Education and Coun-
seling,79(3), 315319.
Bauer, S., Percevic, R., Okon, E., Meermann, R., & Kordy, H. (2003). Use of text messaging in the aftercare of
patients with bulimia nervosa. European Eating Disorders Review,11, 279290.
Boschen, M.J., & Casey, L.M. (2008). The use of mobile telephones as adjuncts to cognitive behavioral psychother-
apy. Professional Psychology: Research and Practice,39(5), 546552.
Both, F., Hoogendoorn, M., Klein, M., & Treur, J. (2009). Design and analysis of an ambient intelligent system
supporting depression therapy. Proceedings of the Second International Conference on Health Informatics, 142148.
Retrieved January 28, 2010, from
Bramley, D., Riddell, T., Whittaker, R., Corbett, T., Lin, R.B., Wills, M., et al. (2005). Smoking cessation using
mobile phone text messaging is as effective in Maori as non-Maori. The New Zealand Medical Journal,118
(1216), U1494.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research Psychology,3(2), 77101.
Cavanagh, K. (2010). Turn on, tune in and (dont) drop out: Engagement, adherence, attrition, and alliance with
internet-based interventions. In D.R. James Bennett-Levy, Paul Farrand, Helen Christensen, Kathy Griffiths,
David Kavanagh, Britt Klein, Mark A. Lau, Judy Proudfoot, Lee Ritterband, Jim White & Chris Williams
(Eds.), Oxford Guide to Low Intensity CBT Interventions (pp. 227233). Oxford: OUP.
Christensen, H., Griffiths, K.M., & Korten, A. (2002). Web-based cognitive behavior therapy: Analysis of site usage
and changes in depression and anxiety scores. Journal of Medical Internet Research,4(1), e3.
Collins, R.L., Kashdan, T.B., & Gollnisch, G. (2003). The feasibility of using cellular phones to collect ecological
momentary assessment data: Application to alcohol consumption. Experimental & Clinical Psychopharmacology,
11(1), 7378.
Cuijpers, P., Marks, I.M., van Straten, A., Cavanagh, K., Gegad, L., & Anderssonef, G. (2009). Computer-aided
psychotherapy for anxiety disorders: A meta-analytic review. Cognitive Behavioral Therapy,38(2), 6682.
522 V. Harrison et al.
J Ment Health Downloaded from by University of New South Wales on 02/13/12
For personal use only.
Ericsson. (2010). Mobile subscriptions hit 5 billion mark, Press release. Retrieved January 28, 2010, from http://www1.
Farvolden, P., Denisoff, E., Selby, P., Bagby, R.M., & Rudy, L. (2005). Usage and longitudinal effectiveness of a
Web-based self-help cognitive behavioral therapy program for panic disorder. Journal of Medical Internet Research,
7(1), e7.
Ferrer-Roca, O., Cardenas, A., Diaz-Cardama, A., & Pulido, P. (2004). Mobile phone text messaging in the man-
agement of diabetes. Journal of Telemedicine & Telecare,10(5), 282285.
Franklin, V., Waller, A., Pagliari, C., & Greene, S. (2003). Sweet Talk: Text messaging support for intensive
insulin therapy for young people with diabetes. Diabetes Technology & Therapeutics,5(6), 991996.
Gee, P., Coventry, K.R., & Birkenhead, D. (2005). Mood state and gambling: Using mobile telephones to track
emotions. British Journal of Psychology,96,5366.
Gosling, S.D., Rentfrow, P.J., & Swann, W.B.J. (2003). A very brief measure of the Big-Five personality domains.
Journal of Research in Personality,37, 504528.
Graham, C., Franses, A., Kenwright, M., & Marks, I. (2000). Psychotherapy by computer: A postal survey of re-
sponders to a teletext article. The Psychiatrist,24, 331332.
Griffiths, K.M., & Christensen, H. (2006). Review of randomised controlled trials of Internet interventions for
mental disorders and related conditions. Clinical Psychologist,10(1), 1629.
Hurling, R., Catt, M., De Boni, M., Fairley, B.W., Hurst, T., Murray, P., et al. (2007). Using Internet and mobile
phone technology to deliver an automated physical activity program: Randomized controlled trial. Journal of
Medical Internet Research,9(2), 112.
Joo, N.S., & Kim, B.T. (2007). Mobile phone short message service messaging for behaviour modification in a com-
munity-based weight control programme in Korea. Journal of Telemedicine and Telecare,13, 416420.
Kwon, H.S., Cho, J.H., Kim, H.S., Lee, J.H., Song, B.R., Oh, J.A., et al. (2004). Development of web-based dia-
betic patient management system using short message service (SMS). Diabetes Research & Clinical Practice,66
(S1), S133S137.
Logan, A.G., McIsaac, W.J., Tisler, A., Irvine, M.J., Saunders, A., Dunai, A., et al. (2007). Mobile phone-based
remote patient monitoring system for management of hypertension in diabetic patients. American Journal of
Hypertension,20(9), 942948.
Lovibond, S.H., & Lovibond, P.F. (1995). Manual for the Depression Anxiety Stress Scales. Sydney: Psychology
Márquez, C.E., de la Figuera von Wichmann, M., Gil Guillen, V., Ylla-Catala, A., Figueras, M., Balana, M., et al.
(2004). Effectiveness of an intervention to provide information to patients with hypertension as short text mess-
ages and reminders sent to their mobile phone. Atencion Primaria,34(8), 399405.
Melville, K.M., Casey, L.M., & Kavanagh, D.J. (2010). Dropout from Internet-based treatment for psychological
disorders. British Journal of Clinical Psychology,49(4), 455471. doi:10.1348/014466509x472138
Morris, M.E., & Guilak, F. (2009). Mobile heart health: Project highlight. IEEE Pervasive Computing,8(2), 5761.
Morris, M.E., Kathawala, Q., Leen, T.K., Gorenstein, E.E., Guilak, F., Labhard, M., et al. (2010). Mobile therapy:
Case study evaluations of a cell phone application for emotional self-awareness. Journal of Medical Internet
Research,12(2), e10.
Mundt, J.C., Marks, I.M., Greist, J.H., & Shear, K. (2002). The work and social adjustment scale: A simple accurate
measure of impairment in functioning. The British Journal of Psychiatry,180, 461464.
Obermayer, J.L., Riley, W.T., Asif, O., & Jean-Mary, J. (2004). College smoking-cessation using cell phone text
messaging. Journal of American College Health,53(2), 7178.
Oliver, M.I., Pearson, N., Coe, N., & Gunnell, D. (2005). Help-seeking behaviour in men and women with
common mental health problems: Cross-sectional study. The British Journal of Psychiatry,186, 297301.
Ostojic, V., Cvoriscec, B., Ostojic, S.B., Reznikoff, D., Stipic-Markovic, A., & Tudjman, Z. (2005). Improving
asthma control through telemedicine: A study of short-message service. Telemedicine Journal & e-Health,11
(1), 2835.
Proudfoot, J., Nicholas, J. (2010). Monitoring and evaluation in low intensity CBT interventions. In J. Bennett-
Levy, D. Richards, P. Farrand, H. Christensen, K. Griffiths, D. Kavanagh, B. Klein, M. Lau, J. Proudfoot,
L. Ritterband, J. White, & C. Williams (Eds.), Oxford Guide to Low Intensity CBT Interventions (pp. 97104).
Oxford: Oxford University Press.
Proudfoot, J., Parker, G., Pavlovic, D.H., Manicavasagar, V., Adler, E., & Whitton, A. (2010). Community atti-
tudes to the appropriation of mobile phones for monitoring and managing depression, anxiety and stress.
Journal of Medical Internet Research,12(5), e64, 6112.
Puccio, J.A., Belzer, M., Olson, J., Martinez, M., Salata, C., Tucker, D., et al. (2006). The use of cell phone remin-
der calls for assisting HIV-infected adolescents and young adults to adhere to highly active antiretroviral therapy:
A pilot study. AIDS Patient Care STDS,20(6), 438444.
Mobile mental health 523
J Ment Health Downloaded from by University of New South Wales on 02/13/12
For personal use only.
Rami, B., Popow, C., Horn, W., Waldhoer, T., & Schober, E. (2006). Telemedical support to improve glycemic
control in adolescents with type 1 diabetes mellitus. European Journal of Pediatrics,165(10), 701705.
Reid, S.C., Kauer, S.D., Dudgeon, P., Sanci, L.A., Shrier, L.A., & Patton, G.C. (2009a). A mobile phone program
to track young peoples experiences of mood, stress and coping. Development and testing of the mobiletype
program. Social Psychiatry and Psychiatric Epidemiology,44(6), 501507.
Reid, S.C., Sanci, L.A., & Patton, G.C. (2009b). Improving the Engagement, Detection and Management of Adolescent
Depression: Applying the Mobiletype Program to General Practice Settings, Report. Melbourne: Beyond Blue. Re-
trieved January 28, 2011, from
Robinson, S., Perkins, S., Bauer, S., Hammond, N., Treasure, J., & Schmidt, U. (2006). Aftercare intervention
through text messaging in the treatment of bulimia nervosa feasibility pilot. International Journal of Eating Dis-
orders,39(8), 633638.
Rodgers, A., Corbett, T., Bramley, D., Riddell, T., Wills, M., Lin, R.B., et al. (2005). Do u smoke after txt? Results
of a randomised trial of smoking cessation using mobile phone text messaging. Tobacco Control,14, 255261.
Shapiro, J.R., Bauer, S., Andrews, E., Pisetsky, E., Bulik-Sullivan, B., Hamer, R.M., et al. (2009). Mobile therapy:
Use of text-messaging in the treatment of bulimia nervosa. International Journal of Eating Disorders,43(6),
Sorbi, M.J. (2009). Mobile Monitoring and Coaching to Support Migraine Self-Management Training Face-To-Face and
Through the Internet. Paper presented at the E-Mental Health Summit. Retrieved January 28, 2010, from http://
Stasiak, K., Merry, S.N., Whittaker, R., Doherty, I., Dorey, E., Chao, P.P., et al. (2010). Positive Psychology + Pre-
vention + Phone = :). Paper presented at the European Conference on Positive Psychology, Abstract (p 105).
Retrieved January 28, 2010, from
Stone, A.A., Shiffman, S., Schwartz, J.E., Broderick, J.E., & Hufford, M.R. (2002). Patient non-compliance with
paper diaries. British Medical Journal 324(7347), 11931194.
Stone, A.A., Shiffman, S., Schwartz, J.E., Broderick, & J.E., Hufford, M.R. (2003). Patient compliance with paper
and electronic diaries. Controlled Clinical Trials,24(2), 182199.
Thiele, C., Laireiter, A.R., & Baumann, U. (2002). Diaries in clinical psychology and psychotherapy: A selective
review. Clinical Psychology and Psychotherapy,9,137.
Wang, P.S., Demler, O., & Kessler, R.C. (2002). Adequacy of treatment for serious mental illness in the United
States. American Journal of Public Health,92,9298.
Whittaker, R., Maddison, R., McRobbie, H., Bullen, C., Denny, S., Dorey, E., et al. (2008). A multimedia mobile
phone-based youth smoking cessation intervention: Findings from content development and piloting studies.
Journal of Medical Internet Research,10(5), e49.
World Health Organisation. (2011). mHealth: New horizons for health through mobile technologies, Report. Retrieved
June 15, 2011, from
524 V. Harrison et al.
J Ment Health Downloaded from by University of New South Wales on 02/13/12
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... Digital transformation is very necessary in smart health field and it associated with the fourth mechanical upheaval for example intermingling of technology and science, hyper-connectivity, and super intelligence [2,3] in the industry of healthcare. In recent years a technology is emerged with the combination of cloud computing [4], big data technologies [5,6], Internet of Things (IoT) [7], and existing types of data and correspondence innovation (ICT) [8,9]. The example of this type of technology are "mobile health" [m-health] [8,9], tele-health," [12][13][14][15] and "u-health" [10,11]. ...
... In recent years a technology is emerged with the combination of cloud computing [4], big data technologies [5,6], Internet of Things (IoT) [7], and existing types of data and correspondence innovation (ICT) [8,9]. The example of this type of technology are "mobile health" [m-health] [8,9], tele-health," [12][13][14][15] and "u-health" [10,11]. IoT empowers physical gadgets to gather and trade information in a mechanized manner. ...
If there is no health record history of a patient then it will be very difficult for treating in different hospitals. Especially for travelers and in the case of emergency it can ruin the auspicious treatment and diagnosis. If the smart health records are based on Cloud then there is higher difficulties for protection and security, the open difficulties of interoperability and joining and also lack of every day support for the high accessibility of wellbeing history. The smart health records that are already exists can store only limited about of data of patients and they stored data only for specific hospital and do not support versatility of patients crosswise over various medical clinics. The technology that use better treatment for patients, improve quality of life for everyone and provide better diagnostic tools can be consider as smart health.
... Online and smartphone-based modalities of delivery facilitate new solutions for disseminating evidence-based smoking cessation treatments in an easy, rapid, and lowcost manner, providing novel opportunities to overcome smoking-related health disparities [10,[14][15][16]. One promising strategy involves ecological momentary interventions (EMIs) delivered through personal digital assistants and mobile phone apps [17][18][19]. EMIs aim to provide the right type, timing, and intensity of treatment in real time (in the context of daily life), based on ecological momentary assessments (EMAs) of relevant variables such as contexts, emotional states, attitudes, perceptions, exposures, or events in daily life [20][21][22]. EMAs assess momentary changes in individuals at random or pre-specified times, and their results can be used to initiate EMIs in the moment they are needed, such as when the user is most likely to lapse or relapse [20,22]. ...
... In recent years, the dramatic growth in mobile phone ownership and their extensive usage have offered an unprecedented delivery platform for EMIs [17,18,25,26]. In addition, advances in mobile technology have laid the foundation to implement novel EMIs mirroring features of in-person interventions in an engaging and dynamic manner [20,27]. ...
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Background and objectives Tobacco use is an important cause of preventable mortality and morbidity worldwide. Only 7% of smokers successfully quit annually, despite numerous evidence-based smoking cessation treatments. An important reason for failure is barriers to accessing appropriate smoking cessation interventions, which can be minimized by technology-delivered interventions, such as ecological momentary interventions. Ecological momentary interventions provide the right type and intensity of treatment in real time, based on ecological momentary assessments of relevant variables. The aim of this review was to assess the effectiveness of ecological momentary interventions in smoking cessation. Methods We searched MEDLINE, Scopus, CENTRAL, psychINFO, and ProQuest without applying any filters on 19 September, 2022. One author screened search results for obvious irrelevant and duplicate studies. The remaining studies were independently reviewed by two authors to exclude irrelevant studies, and then they extracted data from the included studies. We collated study findings, transformed data into a common rubric, and calculated a weighted treatment effect across studies using Review Manager 5. Findings We analyzed 10 studies with a total of 2391 participants. Assessment methods included exhaled CO analyzers, bidirectional SMS, data input in apps, and hand movement detection. Interventions were based on acceptance and commitment therapy and cognitive behavioral therapy. Smoking abstinence was significantly higher in participants of intervention groups compared to control groups (RR = 1.24; 95% CI 1.07–1.44, P = 0.004; I² = 0%). Conclusion Ecological momentary intervention is a novel area of research in behavioral science. The results of this systematic review based on the available literature suggest that these interventions could be beneficial for smoking cessation.
... The advantages of mHealth programs are widely cited and include: the potential for scale (Heron and Smyth 2010), convenience (Atienza and Patrick 2011), customisation (Harrison et al. 2011), quick delivery (Hall, Cole-Lewis, and Bernhardt 2015), instant feedback (Hamine et al. 2015), low distribution costs (Iribarren et al. 2017), low technological barriers to use (Whittaker et al. 2012), and broad population reach (Dobson et al. 2017). ...
... The advantages of mHealth programs are widely cited and include: the potential for scale (Heron and Smyth, 2010), convenience (Atienza and Patrick, 2011), customization (Harrison et al, 2011), quick delivery (Hall et al, 2015), instant feedback (Hamine et al, 2015), low distribution costs (Iribarren et al, 2017), low technological barriers to use (Whittaker et al, 2012), and broad population reach (Dobson et al, 2017). ...
The rising surge of work-related stress is particularly severe among small-to-medium-sized enterprise (SME) owner-managers, and has been linked to an array of deleterious consequences, such as burnout, venture failure, and suicide. Sadly, the majority of available stress management interventions appear to be ill-suited to owner-managers. Prior work, however, suggests that mobile phone-based messaging conversational agents (CAs) may hold promise for delivering mental health interventions to underserved groups such as the self-employed. Furthermore, recent research with SMEs finds that altering one’s stress mindset—beliefs about the extent to which stress might be enhancing or debilitating can change one’s responses to stress. Against this backdrop, the present study assessed the effectiveness and acceptability of a first-of-its-kind mobile messaging-based conversational agent-led stress mindset intervention (mCASMI) for Aotearoa New Zealand SME owner-managers. The mCASMI was delivered over 4-days via WhatsApp Messenger. The results confirmed that the mCASMI was successful in altering participants’ mindsets about stress, in conjunction with self-reported improvements in productivity and work performance. The owner-managers who received the intervention were engaged, adherent, and reported a high degree of rapport with the CA. Though preliminary, these findings extend the state-of-the-art and suggest progressing with a larger-scale feasibility study.
... Another feature is related to real-time management, which allows learning and applying coping strategies in ecologically valid contexts. Among mental health apps aiming to enhance the coping abilities of clients, a few can use the real-time capabilities of smartphones (Donker et al., 2013;Harrison et al., 2011). ...
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Functional mobile applications are regarded as one of the brand-new approaches to psychological interventions in different areas, including stress. The study aims to analyze a mobile application for Persian users on perceived stress and improve mindful skills. This study was a one-group pretest-posttest design with a quasi-experimental research design. Three instruments were used in this study: the Five Factor Mindfulness Questionnaire (FFMQ), the Perceived Stress Scale (PSS-10), and the Mobile App Rating Scale (MARS). Eighty-five students participated in an 8-week mindfulness mobile app (Aramgar) for at least 10 minutes per day intervention. They responded to validated outcome measures of stress and mindfulness at baseline after the 8-week continued access period. The mobile application, Aramgar, was designed based on Mindfulness-based stress reduction. Paired t-tests showed significant differences in general perceived stress (P = 0.03) and total score of mindfulness (P = 0.002) before and after Aramgar. The results of analyzing the quality of Aramgar in terms of engagement, functionality, aesthetics, and information quality showed that specialists assessed the quality of the application appropriately. Using functional mobile apps provides ease of use for mental health services. Therefore, to strengthen and develop the mentioned services, it is recommended that the necessary information technology infrastructures be provided and the existing limitations for designing and running mental health mobile apps be removed.
... Therefore, before making a final decision on the content and processes of finalizing a full 16 week 1616 program, we conducted a Proof-of-Concept (PoC) evaluation to assess the impact and effectiveness of the program on a shorter time scale and with an accessible sample [20]. Importantly, PoC trials are a resource-effective way to demonstrate (a) preliminary efficacy on target behavioral mechanisms (i.e., outcome evaluation) and (b) implementation effectiveness (i.e., process evaluation) to inform the development of more comprehensive iterations and rigorous evaluations of a program [21,22]. The purpose of our PoC, then, was to collect feedback to improve program content and delivery as well as gain certainty that the concepts and storybased intervention approach were valuable to the participants. ...
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The 1616 Program is a newly developed and evidence-informed story-based positive youth development (PYD) program for young ice hockey players (10–12 years of age) in North America. The program uses elite ice hockey players as role models—through story-telling—to serve as inspirational figures to engage youth athletes and important social agents (i.e., parents, coaches) with evidence-informed PYD concepts. The objective of this study was to use a Proof-of-Concept evaluation to assess whether the 1616 Program ‘worked’ in enhancing PYD outcomes and to determine if the concepts were engaging and enjoyable for youth, their parents, and coaches. The 5 week Proof-of-Concept evaluation was conducted with 11 ice hockey teams (n = 160 youths, 93 parents, and 11 coaches), encompassing both qualitative (e.g., focus groups) and quantitative (e.g., retrospective pretest-posttest questionnaires) processes and outcome assessments. Results showed that the program was well received by participants and positively impacted the intended outcomes. Overall, the data presented in this Proof-of-Concept evaluation was deemed to support the development and implementation of the full-scale 1616 Program for a more comprehensive evaluation.
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With over one million deaths per year in the world, suicide is a major public health problem that could be significantly reduced by effective prevention programs. E-health tools are of particular interest for primary prevention as they can address a broad population including people unaware of their own risk and provide information and help without the fear of stigma. Our main objective was to define the overall characteristics of an e-health tool for suicide primary prevention in the French general population by defining the characteristics of the IT features; the content of the information delivered; the best way to structure it; and how it should be relayed and by whom. The research was carried out through a literature review and a co-construction phase with stakeholders. Four types of strategies may guide the construction of e-health tools for suicide primary prevention: education and awareness, (self-)screening, accessing support, and mental health coping. They should be accessible on different devices to reach the most users, and language and content should be adapted to the target population and to the issue being addressed. Finally, the tool should be consistent with ethical and quality best practices. The e-health tool StopBlues was developed following those recommendations.
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Background: While the importance of mental health is well-recognized in the field of occupational health, implementation of effective strategies in the workplace has been limited by gaps in infrastructure, program comprehensiveness, coverage, and adherence. The authors developed a Screening, Brief Intervention, and Referral to Treatment (SBIRT) model based occupational mental health intervention, and implemented in a web-based format with a smartphone application. Methods: The SBIRT-based intervention was developed by a multidisciplinary team, including occupational health physicians, nurses, psychiatrists, and software developers. The following mental health areas were included, based on outcomes of an epidemiological survey conducted: insomnia, depression, anxiety, problematic alcohol use, and suicidal risk. The viability of the two-step evaluation process utilizing a combination of the brief version and the full-length version of the questionnaire was examined using responses from the survey. The intervention was adjusted according to the survey results and expert opinions. Results: The epidemiological survey included 346 employees who completed the long-form version of mental health scales. These data were the used to confirm the diagnostic value of using a combination of short-form and long-form version of the scales for screening in the SBIRT model. The model uses a smartphone application for screening, provision of psychoeducation, and for surveillance. The universal methods of the model ensure it can be implemented by all occupational managers, regardless of their specialization in mental health. In addition to the two-step screening procedure to identify employees at-risk for mental health problems, the model includes a stepped care approach, based on risk stratification, to promote mental health education, management, and follow-up for continuous care. Conclusion: The SBIRT model-based intervention provides an easy-to-implement approach for the management of mental health in the workplace. Further studies are required to examine the effectiveness and feasibility of the model.
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Doctors often ask patients to recall recent health experiences, such as pain, fatigue, and quality of life.1 Research has shown, however, that recall is unreliable and rife with inaccuracies and biases.2 Recognition of recall's shortcomings has led to the use of diaries, which are intended to capture experiences close to the time of occurrence, thus limiting recall bias and producing more accurate data.3The rationale for using diaries would be undermined if patients failed to complete diaries according to protocol. In this study we used a newly developed paper diary that could objectively record when patients made diary entries in order to compare patients' reported and actual compliance with diary keeping. For comparison, we also used an electronic diary designed to enhance compliance in order to assess what compliance rates might be achieved. Methods and results We recruited 80 adults with chronic pain (pain for ≥3 hours a day and rated ≥4 on a 10 point scale) and assigned 40 to keeping a paper diary and 40 to an electronic diary. On satisfying the eligibility criteria, each patient was assigned to the next training session for which he or she was available, regardless of which diary it was for. We conducted one training session for each diary each week, with each training session for the paper diary matched by time and day of the week with an electronic diary training session. Participants were paid $150 and gave their informed consent; patients given the paper diary were not told that compliance would be recorded electronically. The paper diary comprised diary cards bound into a DayRunner Organizer binder. The cards contained 20 questions drawn from several common pain instruments and included fields to record time and date of completion. The diary binders were unobtrusively fitted with photosensors that detected light and recorded when the binder was opened and closed; these were extensively tested and validated. The electronic diary was a Palm computer with software for data collection in clinical trials and presented identical pain questions via a touch screen and recorded time and date of entries. This system (invivodata) incorporated several features to maximise compliance, including auditory prompts, and has demonstrated good compliance.4 Patients were instructed to complete daily entries at 10 am, 4 pm, and 8 pm within 15 minutes of the target times. With the electronic diary, entries could not be initiated outside the designated 30 minute windows. We considered paper diary entries to be compliant if they were made within the 30 minute windows. A more liberal secondary outcome allowed a 90 minute window around the target times. Reported compliance was based on the time and date that patients recorded on their paper diary cards. Actual compliance was based on the electronic record (from the record of diary binder openings for paper diaries). Paper diary entries were deemed compliant if the binder was opened or closed at any point during the target time window. We also assessed “hoarding” with the paper diary, defined as days when the diary binder was not opened but for which diary cards were completed. Compliance rates for 80 patients' record keeping in paper and electronic diaries
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The present paper gives a review of diary methods in clinical psychology and psychotherapy. After a brief discussion of the history and the development of the diary method a description of the methodology and technology of diaries in their scientific and practical uses is given. The main part of the paper presents an overview of instruments used in research and practice in clinical psychology concerning mood and affectivity, anxiety disorders, pain, psycho-physiological states, eating disorders and alcoholism. Additionally, methodological advantages and problems of these tools are discussed. Finally strategies for the assurance and enhancement of the methodological quality of diary data are discussed. The paper comes to several conclusions: (1) although many instruments have been developed during recent years for research and practical aims, they are concerned with only a few of the many clinical phenomena; (2) although some diaries achieve favourable psychometric results, the reliability and validity of most of the instruments has not yet been explored in a satisfactory manner; (3) although most diaries would also be useful for practical reasons, many deal with research aims. Therefore, future work should focus on practical use as well as on psychometric testing. Copyright © 2002 John Wiley & Sons, Ltd.
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AIMS AND METHOD Computerised self-help psychotherapies are fast becoming part of psychiatric practice. The aim of the study was to assess potential user preferences for the delivery of self-help psychotherapy for obsessive—compulsive disorder (OCD) and phobic anxiety disorders. A postal survey was undertaken of enquirers responding to a teletext article on self-help psychotherapies for OCD and agoraphobia. Subjects were asked their preferences for the delivery of self-help services for anxiety disorders, their acceptance or refusal of general practitioner (GP) referrals for such therapy, and how much they would be willing to pay for such a service. RESULTS Of 326 questionnaires sent out 113 completed questionnaires were returned (35%). Twenty-seven per cent of respondents did not wish to access such services via their GP, 91% wanted access via a computer system and respondents were willing to pay an average of £10 per computer session (range 0-100). CLINICAL IMPLICATIONS Computerised self-help psychotherapies for OCD, phobic anxiety disorders and depression are becoming part of everyday clinical practice. This may be the first survey directly asking potential users about their preferred access to self-help psychotherapies for anxiety disorders. A significant proportion of responders did not wish to go via their GP to receive therapy and the vast majority welcomed therapy delivered by some form of computer system.
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Internet-based psychotherapeutic interventions have been used for more than a decade, but no comprehensive review and no extensive meta-analysis of their effectiveness have been conducted. We have collected all of the empirical articles published up to March 2006 (n = 64) that examine the effectiveness of online therapy of different forms and performed a meta-analysis of all the studies reported in them (n = 92). These studies involved a total of 9,764 clients who were treated through various Internet-based psychological interventions for a variety of problems, whose effectiveness was assessed by different types of measures. The overall mean weighted effect size was found to be 0.53 (medium effect), which is quite similar to the average effect size of traditional, face-to-face therapy. Next, we examined interacting effects of various possible relevant moderators of the effects of online therapy, including type of therapy (self-help web-based therapy versus online communication-based etherapy), type of outcome measure, time of measurement of outcome (post-therapy or follow-up), type of problem treated, therapeutic approach, and communication modality, among others. A comparison between face-to-face and Internet intervention as reported on in 14 of the studies revealed no differences in effectiveness. The findings of this meta-analysis, and review of additional Internet therapy studies not included in the meta-analysis, provide strong support for the adoption of online psychological interventions as a legitimate therapeutic activity and suggest several insights in regard to its application. Limitations of the findings and recommendations concerning Internet-based therapy and future research are discussed.
Chapter 7 draws attention to the different functions that patient outcome monitoring has with respect to risk management and the continued evaluation of patient progress. Indeed, monitoring patient risk and progress is a particularly significant aspect of the LI clinical method given that the approach does not require direct contact between the practitioner and patient, but may be mediated solely through the self-help approach itself. The chapter also highlights how monitoring serves as an intervention in its own right. This indicates that the effectiveness of the LI interventions may not solely be accounted for by the self-help interventions, but that the LI clinical method may also make a contribution. To date, little research has been undertaken to disentangle the relative contributions of the clinical method and the self-help interventions with respect to effectiveness.
Chapter 21 explores client engagement and the reasons why clients may or may not take up an internet intervention. Typically, engagement is affected by an interplay of factors associated with the intervention itself (such as its attractiveness and ease of navigation), as well as aspects specific to the individual client, such as his/her expectations about the benefits of the program and perceptions about the time, effort, skill level, and financial cost that will be involved in using it. This chapter recommends that, in order to facilitate engagement, LI practitioners should explore clients’ views about receiving treatment via the internet and address any concerns that they may have. Once clients are engaged, the next important step is to promote adherence to and completion of the LI internet intervention. Evidence suggests that brief weekly support increases adherence and completion of online treatments.
Background Health planning should be based on data about prevalence, disability and services used. Aims To determine the prevalence of ICD-10 disorders and associated comorbidity, disability and service utilisation. Method We surveyed a national probability sample of Australian households using the Composite International Diagnostic Interview and other measures. Results The sample size was 10 641 adults, response rate 78%. Close to 23% reported at least one disorder in the past 12 months and 14% a current disorder. Comorbidity was associated with disability and service use. Only 35% of people with a mental disorder in the 12 months prior to the survey had consulted for a mental problem during that year, and most had seen a general practitioner. Only half of those who were disabled or had multiple comorbidity had consulted and of those who had not, more than half said they did not need treatment. Conclusions The high rate of not consulting among those with disability and comorbidity is an important public health problem. As Australia has a universal health insurance scheme, the barriers to effective care must be patient knowledge and physician competence.
Self-help Internet interventions have the potential to enable consumers to play a central role in managing their own health. This paper contains a systematic review of 15 randomised controlled trials of the effectiveness of self-help Internet interventions for mental disorders and related conditions. Conditions addressed by the interventions included: depression, anxiety, stress, insomnia, headache, eating disorder and encopresis. Most interventions were reported to be effective in reducing risk factors or improving symptoms, although many of the studies had methodological limitations. Three of the interventions that reported positive outcomes are available without charge to the public.