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Mental Health Smartphone Apps: Review and Evidence-Based Recommendations for Future Developments

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Background: The number of mental health apps (MHapps) developed and now available to smartphone users has increased in recent years. MHapps and other technology-based solutions have the potential to play an important part in the future of mental health care; however, there is no single guide for the development of evidence-based MHapps. Many currently available MHapps lack features that would greatly improve their functionality, or include features that are not optimized. Furthermore, MHapp developers rarely conduct or publish trial-based experimental validation of their apps. Indeed, a previous systematic review revealed a complete lack of trial-based evidence for many of the hundreds of MHapps available. Objective: To guide future MHapp development, a set of clear, practical, evidence-based recommendations is presented for MHapp developers to create better, more rigorous apps. Methods: A literature review was conducted, scrutinizing research across diverse fields, including mental health interventions, preventative health, mobile health, and mobile app design. Results: Sixteen recommendations were formulated. Evidence for each recommendation is discussed, and guidance on how these recommendations might be integrated into the overall design of an MHapp is offered. Each recommendation is rated on the basis of the strength of associated evidence. It is important to design an MHapp using a behavioral plan and interactive framework that encourages the user to engage with the app; thus, it may not be possible to incorporate all 16 recommendations into a single MHapp. Conclusions: Randomized controlled trials are required to validate future MHapps and the principles upon which they are designed, and to further investigate the recommendations presented in this review. Effective MHapps are required to help prevent mental health problems and to ease the burden on health systems.
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Review
Mental Health Smartphone Apps: Review and Evidence-Based
Recommendations for Future Developments
David Bakker1, B Psych (Hons); Nikolaos Kazantzis1,2, PhD; Debra Rickwood3, BA (Hons), PhD; Nikki Rickard4,
BBSc(Hons), PhD(Psych)
1School of Psychology and Monash Institute of Cognitive and Clinical Neurosciences, Faculty of Medicine, Nursing and Health Sciences, Monash
University, Clayton, Australia
2Cognitive Behaviour Therapy Research Unit, School of Psychological Sciences, Faculty of Medicine, Nursing and Health Sciences, Monash University,
Clayton, Australia
3Psychology Department, Faculty of Health, University of Canberra, Canberra, Australia
4Centre for Positive Psychology, Melbourne Graduate School of Education, University of Melbourne, Parkville, Australia
Corresponding Author:
David Bakker, B Psych (Hons)
School of Psychology and Monash Institute of Cognitive and Clinical Neurosciences
Faculty of Medicine, Nursing and Health Sciences
Monash University
18 Innovation Walk
Wellington Road
Clayton, 3800
Australia
Phone: 61 3 9905 4301
Fax: 61 3 9905 4302
Email: david.bakker@monash.edu
Abstract
Background: The number of mental health apps (MHapps) developed and now available to smartphone users has increased in
recent years. MHapps and other technology-based solutions have the potential to play an important part in the future of mental
health care; however, there is no single guide for the development of evidence-based MHapps. Many currently available MHapps
lack features that would greatly improve their functionality, or include features that are not optimized. Furthermore, MHapp
developers rarely conduct or publish trial-based experimental validation of their apps. Indeed, a previous systematic review
revealed a complete lack of trial-based evidence for many of the hundreds of MHapps available.
Objective: To guide future MHapp development, a set of clear, practical, evidence-based recommendations is presented for
MHapp developers to create better, more rigorous apps.
Methods: A literature review was conducted, scrutinizing research across diverse fields, including mental health interventions,
preventative health, mobile health, and mobile app design.
Results: Sixteen recommendations were formulated. Evidence for each recommendation is discussed, and guidance on how
these recommendations might be integrated into the overall design of an MHapp is offered. Each recommendation is rated on the
basis of the strength of associated evidence. It is important to design an MHapp using a behavioral plan and interactive framework
that encourages the user to engage with the app; thus, it may not be possible to incorporate all 16 recommendations into a single
MHapp.
Conclusions: Randomized controlled trials are required to validate future MHapps and the principles upon which they are
designed, and to further investigate the recommendations presented in this review. Effective MHapps are required to help prevent
mental health problems and to ease the burden on health systems.
(JMIR Mental Health 2016;3(1):e7) doi:10.2196/mental.4984
KEYWORDS
mobile phones; mental health; smartphones; apps; mobile apps; depression; anxiety; cognitive behavior therapy; cognitive
behavioral therapy; clinical psychology
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Introduction
A smartphone is an advanced mobile phone that functions as a
handheld computer capable of running software apps. Within
the last decade, smartphones have been integrated into the
personal, social, and occupational routines of a substantial
proportion of the global population. Over half of the population
in the United States owns a smartphone and 83% of these users
do not leave their homes without it [1]. Average users check
their phones as often as 150 times a day [2], which reflects how
smartphone apps can generate, reward, and maintain strong
habits involving their use [3,4]. Apps are also capable of
implementing behavior change interventions [5], which may
improve users’ physical health [6], such as through promotion
of physical exercise [7].
Over recent years, numerous mental health apps (MHapps) have
been developed and made available to smartphone users. These
apps aim to improve mental health and well-being, ranging from
guiding mental illness recovery to encouraging beneficial habits
that improve emotional health [8]. The demand for MHapps is
strong, as evidenced by a recent public survey that found that
76% of 525 respondents would be interested in using their
mobile phone for self-management and self-monitoring of
mental health if the service were free [9].
MHapps and other technology-based solutions have the potential
to play an important part in the future of mental health care [10],
making mental health support more accessible and reducing
barriers to help seeking [11]. Innovative solutions to
self-management of mental health issues are particularly
valuable, given that only a small fraction of people suffering
from mood or anxiety problems seek professional help [12].
Even when people are aware of their problems and are open to
seeking help, support is not always easily accessible,
geographically, financially, or socially [13].
Smartphones are not constrained by geography and are usually
used privately by one individual. This means that smartphone
apps can be extremely flexible and attractive to users,
empowered by the confidentiality of their engagement. Seeking
help by downloading and using an MHapp is well suited to the
needs of young adults and other users with a high need for
autonomy [14]. Users also prefer self-help support materials if
they are delivered via a familiar medium [15], such as a personal
smartphone. Smartphones apps are almost always accessible to
users, so they can be used in any context and in almost any
environment [16]. Using these apps, users can remind
themselves throughout the day of ongoing goals and motivations,
and be rewarded when they achieve goals [17].
However, many MHapps have not capitalized on the strengths
and capabilities of smartphones. Design principles that have
led to the huge success of many physical health and social
networking apps have not been utilized in the MHapp field.
Furthermore, evidence-based guidelines that have been
developed for other self-help mental health interventions have
not been applied to many MHapps. For example, many available
MHapps target specific disorders and label their users with a
diagnosis. Much research has suggested that this labeling process
can be harmful and stigmatizing [18].
There also appears to be a lack of appreciation for experimental
validation among MHapp developers. Donker et al [8] revealed
that there is a complete lack of experimental evidence for many
of the hundreds of MHapps available. Their systematic review
identified only 5 apps that had supporting evidence from
randomized controlled trials (RCTs). A search of the Apple and
Google app stores as of January 2014 reveals that none of these
RCT-supported apps is currently available to consumers.
For a mental health intervention to be effective, there must be
a process of rigorous experimental testing to guide development
[19]. Appropriate theories of engagement and implementation
should also be consulted when introducing an evidence-based
intervention to the public [20]. However, such research is
currently lacking. A series of recommended principles based
on evidence and substantiated theories would be valuable in
guiding the development of future MHapps and future RCTs.
A review of the literature highlights the numerous ways by
which the design, validation, and overall efficacy of MHapps
could be improved.
Methods
This review aims to provide a set of clear, sound, and practical
recommendations that MHapp developers can follow to create
better, more rigorous apps. As such, this review covers work
from a number of different research fields, including mental
health interventions, preventative health, mobile health, and
mobile app design. A review of currently available MHapps
was also necessary to gain a clearer idea of where improvements
can be made.
Databases such as PsycInfo, Scopus, and ProQuest were
consulted for peer-reviewed sources. Search terms included (but
were not restricted to) “mhealth,” “anxiety,” “depression," “help
seeking,” “self-help, “self-guided,” “smartphones,” and
“gamification.Articles published between March 1975 and
March 2015 were considered for inclusion. Meta-analyses and
systematic reviews were sought for each relevant area of
investigation. Several synoptic texts were also consulted to
guide foundational understanding of theoretical concepts relating
to mobile apps and product design [3,5]. Sources were excluded
from the review if they did not relate directly to mental health
or computerized health interventions. Because this was not a
systematic review, and as such was not based on a single search
of the literature, the specific number of articles found and
excluded was not tracked. Furthermore, multiple searches were
used to explore the concepts and formulate the recommendations
presented. The lead author (DB) conducted these searches and
formulated the basic recommendations. The secondary authors
provided individual feedback on the review, suggested sources,
and guided further searches that the lead author undertook.
Most research into mobile health has focused on validating
single entrepreneurial apps, rather than pursuing rigorous RCTs
to validate principles that can guide development of future apps
[21]. Because of the infancy of the field, the recommendations
presented in the results of this review have not been rigorously
validated by RCTs in an MHapp setting. Instead, each
recommendation should be treated as a guide for both
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development of MHapps and future research. Each
recommendation could well be the target of a future RCT.
Currently Available Apps
The recommendations explored in this review should be
considered in the context of the existing range of MHapps
available. The suggested recommendations are as follows: (1)
cognitive behavioural therapy based; (2) address both anxiety
and low mood; (3) designed for use by nonclinical populations;
(4) automated tailoring; (5) reporting of thoughts, feelings, or
behaviors; (6) recommend activities; (7) mental health
information; (8) real-time engagement; (9) activities explicitly
linked to specific reported mood problems; (10) encourage
nontechnology-based activities; (11) gamification and intrinsic
motivation to engage; (12) log of past app use; (13) reminders
to engage; (14) simple and intuitive interface and interactions;
(15) links to crisis support services; (16) experimental trials to
establish efficacy. This is a recommended direction for future
research. To demonstrate the necessity of such a future review
or some form of accreditation system to ensure the quality of
health care apps [22], the lead author conducted a brief overview
of the range of currently available MHapps via a series of
preliminary searches of the iTunes App Store. The search terms
used included “anxiety,“depression,” “low mood,“mental
health,” “therapy,” “relaxation,and “self-help.Inspection and
use of the apps found in these searches revealed some major
gaps in their capabilities when compared with the
recommendations of this review. Table 1 compares a selection
of these apps across the recommended features discussed in this
review.
Results
The recommendations formulated by this review of the literature
are summarized in the following section. Recommendations
1-7 have been chiefly extrapolated from the mental health
literature, and Recommendations 8-14 have origins in research
on user engagement and designing apps for behavior change.
Recommendations 15 and 16 are recommendations specifically
related to MHapps.
It may not be possible to build every single listed
recommendation into a single app. Rather, this list has been
compiled based on the available evidence to guide decisions
when embarking on an MHapp development project. Many
currently available MHapps lack features that would greatly
improve their functionality, or include features that are not
optimized. Thus, the purpose of this review is to collate a list
of easily followed recommendations to be used by developers
when creating future MHapps.
Some of these recommendations will be relevant to informing
both the interface design and the marketing of MHapps. It is
important to note that the marketing of an app is tied to the way
that users will interact with it [23], in the same way that
pretherapy expectations can influence engagement motivation
and hopefulness [24]. For example, if a user downloads an app
because its description on the app store lists “relaxation,” the
user will plan to use the app for relaxation purposes. When app
design is mentioned in the recommendations, this is inclusive
of an app’s marketing.
Recommendations
Cognitive Behavioral Therapy Based
Cognitive behavioral therapy (CBT) is a type of collaborative,
individualized, psychological treatment that is recognized as
the most supported approach to generate behavioral, cognitive,
and emotional adaption to a wide range of common
psychological problems [25]. The efficacy of CBT has been
supported by a comprehensive review of 106 meta-analyses
across different clinical groups [26]. Other meta-analyses have
found strong support for CBT as an effective treatment for a
huge range of psychological disorders, including depression
[27,28], generalized anxiety disorder [29], social anxiety [30],
health anxiety [31], panic disorder [32], posttraumatic stress
disorder [33], obsessive-compulsive disorder [34], phobias, and
anxiety disorders overall [35]. Meta-analytic evidence for CBT
also extends to anger expression problems [36], insomnia [37],
pathological gambling [38], hoarding disorder [39], irritable
bowel syndrome [40], psychosis prevention [41], and
occupational stress [42].
Although CBT’s most researched application is as a therapeutic
technique delivered collaboratively by a trained clinician, its
principles have also been used as the foundation of many
self-help support measures. Using technology is a cost-effective
way to enhance the efficiency of CBT treatment [43,44], and
research has already demonstrated that CBT-based
self-administered computerized interventions are successful for
improving depression and anxiety symptomatology in adults.
A meta-analysis of 49 RCTs revealed a significant medium
effect size (g=0.77, 95% CI 0.59-0.95) for computerized CBT
(CCBT) for depression and anxiety [45]. Another meta-analysis
of 22 RCTs found an even greater effect size (g=0.88, 95% CI
0.76-0.99) [46]. Similar findings for CCBT’s efficacy have
emerged from meta-analyses that have focused on anxiety [47],
depression [48], and its use with young people [49]. CCBT
interventions can be administered by a mobile device and still
retain their therapeutic validity [50]. RCTs have established the
efficacy of CBT-based interventions delivered via smartphone
apps that reduce depression [50], chronic pain [51], and social
anxiety disorder [52]. CBT-based features can also be appealing
to users. In an analysis of features used on a smartphone app
for smoking cessation, 8 of the top 10 used features were CBT
based [53], such as progress tracking and journaling (see the
“Reporting of Thoughts, Feelings, or Behaviors” section).
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Table 1. Currently available iOS apps compared across recommended features.
Recommended featurea
App
16151413121110987654321
b
AnxietyCoach
Behavioral Experiments
c
Breathe
c
DBT Diary Card and Skills Coach
Depression Prevention
b
Happify
HealthyHabits
c
HealthyMinds
HIAF
iCouch CBT
d
iCounselorf
iMoodJournal
In Hand
MindShift
c
MoodKit
c
Moodlytics
c
e
Moody Me
Pacifica
Pocket CBT
SAM
Smiling Mind
Stress & Anxiety Companion
b
SuperBetter
ThinkHappy
What’s Up?
c
WorkOut
WorryTime
aSee the “Currently Available Apps” section for the 16 recommendations.
bNot using automated processes.
cDefault is for reminders to be off.
dOnly because there are separate apps for separate problems, so each app recommends activities for that target problem.
eAccessible via forums
fIncludes separate iCounselor: Depression; iCounselor: Anger; and iCounselor: Anxiety apps.
Although primarily applied in clinical contexts, CBT is also
fundamentally a prevention technique acting to prevent
psychological problems from precipitating or maintaining
clinical disorders [54-56]. This means that CBT-based MHapps
have the potential to be effective for managing both clinical and
subclinical psychological problems [57], provided that such
apps avoid using CBT-based techniques that are used for very
specific clinical psychological problems, are marketed correctly,
and employ well-designed interfaces.
To ensure that an MHapp is indeed CBT based, it is important
to keep the core principles of CBT in mind. Mennin et al [58]
summarize the unifying factors that underlie all CBT approaches
into three change principles: context engagement, attention
change, and cognitive change. Context engagement involves
training clients in a way that promotes more adaptive associative
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learning, which involves having them learn cues for threats and
rewards that are more reasonable and lead to better functioning
than existing cues. This includes CBT techniques that aim to
recondition maladaptive associations, such as exposure and
behavioral activation. The app SuperBetter [59] prescribes
“power-ups” that may incorporate these techniques. Attention
change is the ability to focus attention adaptively on relevant,
nondistressing stimuli. This includes therapeutic processes such
as attention training, acceptance or tolerance training, and
mindfulness. These techniques are employed in Smiling Mind
[60], and can be seen in the meditations displayed in Figure 1.
Finally, cognitive change is the ability to change one’s
perspective on an event, which then affects the emotional
significance and meaning of that event [61]. This includes
metacognitive awareness and cognitive distancing, which are
promoted through therapeutic processes such as decentering or
defusion and cognitive reframing or reappraisal. An example
of this can be found in using the Thoughts tool in MoodKit [62],
as seen in Figure 2. If these three change principles are being
employed to some degree by an intervention, then it can claim
to be based on CBT’s core principles.
To employ these change principles effectively, a therapist and
client must develop a relationship that involves collaborative
empiricism (CE) [63]. CE refers to shared work between client
and practitioner to embed a hypothesis testing approach into
interventions [64]. CE empowers clients to explore their
behaviors and beliefs outside of therapy sessions using
between-session (homework) interventions [65]. A meta-analysis
of studies that compared therapy with and without homework
found an effect size of d=0.48 in favor of using between-session
activities [66]. In the context of CBT-based MHapps, CE may
refer to how the app interacts with the user to complete
therapeutic tasks, and whether it does it in a collaborative,
experimentation-based way. This would ideally involve
encouraging users to develop their own hypotheses about what
may happen as a result of using the app or participating in
certain activities (see the “Recommend Activities” section). An
app that embraces CE is Behavioral Experiments-CBT [67],
which affords users the ability to predict the outcomes of any
behavioral experiments they participate in. Behavioral
experiments are CBT-based challenges that individuals perform
to challenge their own beliefs about the negative outcomes of
various situations [68]. This process of comparing predictions
with actual outcomes can challenge unhelpful beliefs [69].
Self-determination theory (SDT) can aid in understanding CE’s
benefits in CBT [64]. SDT emphasizes the effects of autonomy
and mastery on intrinsic motivation [70]. Intrinsic motivation
is the “prototypic manifestation of the human tendency toward
learning and creativity” [71]. Autonomy feeds this motivation
by affording individuals opportunities for self-direction and
choice [72], and fostering self-efficacy [73]. Self-efficacy and
a feeling of competency lead to a feeling of mastery, which is
an intrinsic reward and motivator in itself [74]. CE and
between-session activities promote autonomy and provide
opportunities for development of competence in behavioral,
emotional, or cognitive self-management. SDT can inform
MHapps on how to best engage users in CBT-based
interventions (ie, by intrinsically motivating them). Users will
be more motivated to engage with apps and products that
encourage autonomy, emphasize user choice, and allow
opportunities for building mastery. For example, SuperBetter
[59] employs SDT-based, game-based principles to intrinsically
motivate users to engage with the app and experience the
well-being-promoting effects of mastery (see the “Gamification
and Intrinsic Motivation to Engage” section).
Figure 1. Screenshot of Smiling Mind displaying meditations.
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Figure 2. Screenshot of MoodKit displaying thought checker.
Address Both Anxiety and Low Mood
Emotional disorders (eg, anxiety and depression) are by far the
most common psychological conditions in the community, with
an estimated 20.9% of US citizens experiencing a major
depressive episode and 33.7% suffering from an anxiety disorder
at some point throughout their lives [75]. Emotional disorders
are also the most treatable [76], but help seeking for sufferers
is very low [77]. There is strong supportive evidence for CCBT
as an effective therapy for reducing symptoms of the most
common anxiety disorders and depression [45,46].
There is an extremely high comorbidity between anxiety and
depression [78], with 85% of people diagnosed with depression
problems also suffering significant anxiety and 90% of people
diagnosed with anxiety disorders suffering significant depression
[79]. In Australia, 25% of all general practice patients have
comorbid depression and anxiety [80]; whereas in Great Britain,
half of all mental illness cases are mixed anxiety and depression
[81]. These two diagnoses share a few major underlying factors
[82]. This raises two important considerations for MHapp
self-help interventions. First, interventions designed for one
disorder are likely to have some efficacy for other emotional
disorders, and second, interventions that target shared underlying
factors across emotional disorders will be more efficacious.
Transdiagnostic CBT (TCBT) is an effective therapeutic
approach that targets the common underlying factors shared by
different psychological disorders. A meta-analysis of RCTs
found a large effect size (standardized mean difference = 0.79,
95% CI 1.30 to 0.27) for TCBT across different anxiety
disorders [83]. Furthermore, TCBT has been found to be
successful in treating depression [25]. Barlow et al’s [84]
Unified Protocol (UP) is a recent TCBT treatment that focuses
on monitoring and adjusting maladaptive cognitive, behavioral,
and emotional reactions that underlie depression and anxiety
disorders. The UP has yielded very promising results across
various emotional disorders, reducing psychopathology [85]
and improving psychological well-being [86]. It is important
to note that TCBT protocols do not imply that all emotional
disorders can be treated effectively with the exact same
techniques [87]. The basic structure for treating different clinical
problems may be relatively uniform, but tailoring of
interventions is still essential (see the “Automated Tailoring”
section), and the structure of TCBT affords flexibility. For
example, the UP consists of four core modules that are designed
to (1) increase present-focused emotional awareness, (2) increase
cognitive flexibility, (3) aid identification and prevention of
patterns of emotion avoidance and maladaptive emotion-driven
behaviors, and (4) promote emotion-focused exposure [88].
This enables a prescriptive approach, whereby certain modules
can be focused on more than others, depending on the needs of
the client or user [88]. An Internet-delivered TCBT intervention
called the Wellbeing Program used a structure of 8 lessons,
focusing on areas such as psychoeducation, thought-monitoring
strategies, behavioral activation, and graded exposure [57]. A
clinician guided users through the program and tailored the
delivery of each lesson to the user’s needs. An RCT supported
the efficacy of this intervention across depression and anxiety
disorders [57]. Although the Wellbeing Program was guided
by a clinician and not via automated processes, many other
self-guided CBT interventions use a transdiagnostic approach
to maximize efficiency and adaptability [89], particularly in an
automated Internet-delivered context [90].
Despite the success of TCBT, many MHapps are designed for
the treatment of specific disorders. Some apps are marketed for
anxiety and others for depression. Few apps acknowledge that
the underlying CBT principles guiding self-help interventions
for anxiety and mood problems are very similar; thus,
broadening the target group of the app can be beneficial for all
users. Combining treatments for both anxiety and depression
into a single app would also reduce the commitment required
for engagement. Users could consolidate their investment within
a single app, instead of dividing their effort and time engaging
with 2 separate apps (one for anxiety and the other for
depression).
Designed for Use by Nonclinical Populations
Many apps have been designed for use with populations who
have been diagnosed with a specific clinical disorder, from
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depression (eg, Optimism [91]) and anxiety (eg, SAM [92]) to
eating disorders (eg, Recovery Record [93]) and borderline
personality disorder (eg, DBT [Dialectical Behavior Therapy]
Diary Card and Skills Coach [94]). Some of these clinical
diagnosis apps are known to be effective for interventions [8],
but they do not capitalize on one of the major advantages of
smartphones: high accessibility. Smartphones are interwoven
into the routines of millions of people all over the world, the
majority of whom have not been diagnosed with a clinical
psychological disorder but do experience unpleasant
psychological distress from time to time. Targeting a specific
clinical population with an MHapp automatically excludes the
majority of smartphone owners from using that app. By contrast,
an MHapp built for a population interested in the prevention of
emotional mental health problems increases the number of
eligible and willing users. A meta-regression of 34 studies found
that self-help interventions were significantly more effective
when recruitment occurred in nonclinical settings (effect size
I2=0.66) than in clinical settings (effect size I2=0.22) [48]. The
field would therefore benefit from more MHapps with
preventative applications that are widely marketable, rigorous,
and effective.
An MHapp market saturated with clinical diagnosis apps also
has the potential to be harmful for help seekers. Users who are
experiencing low-level symptoms of a disorder may feel labeled
by an app that assumes that they have a clinical diagnosis [95].
Self-stigma from this labeling can be harmful, lowering
self-esteem and self-efficacy [96]. Initiatives that acknowledge
the continuum of mental health and the importance of well-being
promotion may reduce stigma and increase help seeking for
mental health problems [97]. Programs such as Opening Minds
[98] aim to reduce mental illness stigma by adopting a
nonjudgmental, nondiagnostic, and nonclinical CBT-based
stance to mental health problems. MHapps that focus on
nonclinical mental health, psychological well-being, or coping
abilities may therefore avoid the harmful effects of labeling
mental illness [99].
CBT is built on the foundation that mental health is a continuum
[89] and that supporting individuals in coping with nonclinical
psychological distress can prevent symptoms from reaching
clinical significance [100]. Furthermore, CBT-based support
can help prevent relapse [101], expand an individual’s coping
skills repertoire [102], and assist individuals experiencing
psychological distress to avoid developing a clinical disorder
[103]. Building a CBT-based MHapp that acknowledges the
continuum of mental health can be used by both clinical and
nonclinical populations.
CBT treatment adopts a formulation-based approach rather than
a diagnosis-based approach [54,104]; as such, a diagnosis is not
necessary for support to be given. Formulation involves
exploring the predisposing, precipitating, perpetuating, and
protective factors connected to a psychological problem, and
then building these factors into a causal model [105].
Conversely, diagnosis relies on detection of symptoms and
fulfillment of criteria statistically linked to a particular disorder
[106]. In many cases, a formal diagnostic label is not important
for informing real-world treatment, and it does not specify the
causal factors contributing to an individual’s unique
psychological problems. Formulation is much more useful
because it can inform exactly which precipitating and
perpetuating factors are contributing to an individual’s unique
psychological problem, and which psychological techniques
can produce optimal solutions [107]. Hofmann [108] proposed
a cognitive behavioral approach for classifying clinical
psychological problems that avoids diagnostic labeling, which
is better at informing CBT-based support because it is based on
formulation. MHapp developers are encouraged to explore
formulation-based approaches to CBT to inform the
development of CBT-based MHapps.
Designing MHapps for nonclinical support may mean adopting
a preventative framework. There are generally three types of
preventative intervention: universal (ie, delivered to everyone
in the community), selective (ie, delivered to at-risk groups),
and indicated (ie, delivered to individuals with preclinical
symptoms) [109]. The flexibility of MHapps means that a single
app could theoretically adapt to any of these three intervention
models, providing a universal intervention as default, and
tailoring to a selective or indicated approach if a user’s responses
suggest that they are at risk of a certain condition.
Some mobile interventions that have been validated and trialed
experimentally were built for personal digital assistants (PDAs)
and not for modern smartphones [7,110]. This severely limits
their nonclinical use and introduces other barriers to routine
engagement that are not experienced by smartphone apps.
However, evidence and principles from PDA-based studies
should be considered when designing smartphone apps.
Automated Tailoring
An advantage of eHealth interventions over other self-help
interventions is their capacity for tailoring [90,111]. Tailoring
in this context refers to the adjustment of technology-delivered
self-help programs to suit the user’s needs, characteristics, and
comorbidities or case formulation [112]. Tailored CCBT
interventions have been shown to be more efficacious than rigid
self-help interventions across a range of depressive and anxiety
disorders [112-115].
Formulation-based tailoring improves the functionality of an
intervention and provides targeted solutions to a user’s
psychological problems. There is a large range of different
self-help mental health interventions available, and selecting
the right intervention can be a challenging and overwhelming
process [15]. The complexity of choices can be simplified or
reduced by building an app capable of automated tailoring,
which combines elements of a large number of different
interventions and deploys them strategically depending on the
needs of individual users. A review of currently available
MHapps reveals, however, that many apps aim to provide a
service but do not service a need [116]. For example, many apps
provide guided meditation, but do not guide users toward
meditation when they are feeling anxious. With tailoring, the
app can recommend users specific solutions to their specific
problems.
Automated tailoring requires the collection of data to identify
the needs of users and develop a functional analysis or case
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formulation. This can be achieved in three main ways. First,
self-report measures can be deployed to elicit in-depth responses
about symptoms and characteristics. Second, data from a user’s
self-monitoring (see “Reporting of Thoughts, Feelings, and
Behaviors” section) can be used to predict the types of
interventions that are well suited to an individual user. Third,
an app’s behavioral usage data can be used to predict which
features of that app a user is using most. If these second and
third data sources are correctly utilized, tailoring can be carried
out seamlessly, without any additional input from the user,
which decreases users’required effort to use the app and thereby
increases app functionality [3].
CBT includes a very wide range of evidence-based techniques
that may be selectively employed by an MHapp depending on
automated tailoring data. For example, if data sources suggest
that the user is experiencing significant physiological arousal,
rather than overwhelming worry or other anxiety-related
problems, CBT techniques such as breathing relaxation may be
recommended over others, based on the available evidence
[117]. Ideally, these therapeutic techniques would be employed
by the MHapp that actually performs the automated tailoring,
but restrictions may mean that the MHapp must rely on referring
users to other apps. This is not ideal, as it may disrupt the user’s
engagement with the MHapp. However, if necessary, any
referrals should be based on a thorough review of the other
existing apps and their supporting evidence [116].
Reporting of Thoughts, Feelings, or Behaviors
Clients who record their own thoughts, feelings, and behaviors
as part of a CBT-based intervention are able to reflect on their
reports and exercise self-monitoring [118]. Self-monitoring is
a core feature of many evidence-based psychological therapeutic
techniques, including CBT [119,120], mindfulness exercises
[121], emotion-focused therapy [122], DBT [123], and
acceptance and commitment therapy (ACT) [124].
Self-monitoring can be used to restructure maladaptive anxiety
responses [125,126], challenge perpetuating factors of
depression [127], and sufficiently treat a small but significant
proportion of posttraumatic stress disorder sufferers [128,129].
Self-monitoring is particularly suitable for CBT-based
interventions that aim to change behavior, with
self-monitoring-only treatment conditions showing benefits for
problem drinking [130] and sleep hygiene [131]. Furthermore,
self-monitoring is a feature of successful weight loss
interventions [132]. Encouraging MHapp users to report their
thoughts, feelings, or behaviors in an objective way should
therefore help promote accurate, beneficial self-monitoring.
Self-monitoring of mood can boost overall emotional
self-awareness (ESA) [133], which can in turn lead to
improvements in emotional self-regulation [134]. Emotional
self-regulation is valuable for individuals in preventing distress
from spiraling out of control and thereby culminating in clinical
problems [135]. Poor emotional awareness is a common
underlying factor for both anxiety and depression [136]. The
ability to differentiate and understand personal emotions, an
integral process in ESA, is positively related to adaptive
regulation of emotions [137] and positive mental health
outcomes [138]. Self-reflection and insight correlate positively
with levels of positive affect and the use of cognitive reappraisal,
and negatively with levels of negative affect and the use of
expressive suppression [139]. Explicit emotion labeling shares
neurocognitive mechanisms with implicit emotion regulation
ability, suggesting that increasing ESA through practicing
labeling of personal emotions will lead to improvements in
emotional regulation and adaptation [140].
Some self-monitoring interventions are limited by problems
related to recall biases. Self-reflection at the end of a day or in
a time and place removed from normal stressors can be
inaccurate [141]. One of the benefits of MHapps is that
smartphones are capable of ecological momentary assessment
(EMA) and experience sampling methods (ESM), which involve
measuring experiences and behavior in real time [142]. MHapp
users can record self-monitoring data on their smartphones while
they are participating in their usual daily routines, undergoing
challenges, or directly experiencing stressors [143]. This can
help reduce bias in self-monitoring [141], thereby improving
the accuracy of users’reflections.
Increasing ESA should lead to greater help seeking, because
factors preventing help seeking include low emotional
competence [144] and low self-awareness [77]. Using
technology for self-monitoring can increase help seeking,
particularly if there is a capacity to contact health professionals
built in to the service [145] (see the “Links to Crisis Support
Services” section).
Self-monitoring via traditional means might also be less
effective for very busy individuals who do not have the time to
complete monitoring entries [118]. MHapps can reduce
monitoring demands by automating some parts of the monitoring
process, such as shifting the burden of some of the more
administrative parts of self-monitoring (eg, entering dates and
times, formatting monitoring entries) from the user to the
smartphone [5]. Using smartphone apps also allows for more
frequent and broader opportunities for recording reflections,
such as while waiting or traveling on public transport.
Keeping all self-reports structured and objective can help users
report quickly and in a format that facilitates data analysis by
the MHapp. It may also reduce some of the barriers to
self-monitoring: for instance, some depressed clients may find
the demands of open-ended self-monitoring overwhelming,
whereas perfectionistic or obsessive clients may spend too much
time and effort on their monitoring [146]. MHapps with highly
structured reporting in a simple interface (see “Simple and
Intuitive Interface and Interactions” section) may be able to
remedy this by limiting the amount of information necessary
for logs, simplifying the monitoring process, reducing the
demands on users, and increasing engagement in the app [5].
Several studies support the efficacy of using app-based
interventions to increase ESA. Morris et al [147] developed an
app that prompted users to report their moods several times a
day. Users reported increases in their ESA, and upon reflection
of their ratings, some were able to recognize patterns of
dysfunction and interrupt these patterns through modification
of routines. Kauer et al [133] used a mobile phone
self-monitoring program to prompt users to report their
emotional state several times throughout the day. Participants
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who reported on their emotional state showed increased ESA
and decreased depressive symptoms compared with controls.
Both of these monitoring systems were, however, quite simple
and offered little constructive feedback to users about their
mood history. They were also trialed on small samples of
individuals who had reported psychological distress. There is,
therefore, a need to further investigate the impact of
smartphone-based mood reporting on ESA and associated mental
health outcomes, using an app that gives better feedback and is
relevant to nonclinical users.
The reporting required for self-monitoring can also enable
feedback and evaluation of therapeutic progress. In
psychological therapy, therapeutic outcomes can be enhanced
by providing clients and clinicians with feedback concerning
treatment progress [148,149]. These positive effects have been
substantiated via a literature review [150] and a meta-analysis,
which found a notable effect size (d=0.10, 95% CI 0.01-0.19)
[151]. MHapps may be able to provide feedback by presenting
a user’s own reporting data back to them, but reframed in
context with the user’s treatment goal. For example, the mood
feedback provided by MoodKit [62] can displayed as a chart,
as shown in Figure 3. This type of feedback-focused progress
tracking relates also to gamification (see the “Gamification and
Intrinsic Motivation to Engage” section) and keeping a log of
past app engagement (see the “Log of Past App Use” section).
Figure 3. Screenshot of MoodKit displaying mood chart.
Recommend Activities
CBT aims to engage clients in a range of activities that are
congruent with its core principles (ie, context engagement,
attention change, and cognitive change) [58]. This represents a
shift away from passive interventions toward ones that actively
engage clients. CBT-based activities that can be recommended
to MHapp users can be summarized into the following
categories: (1) exercise and direct mood improvement, (2)
behavioral activation, and (3) coping skills training.
Activities That Directly Enhance Mood Improvement
A range of activities might target mood directly. For example,
it is well established that increasing physical activity and
promoting exercise can reduce depressive symptoms [152-154]
and anxiety [155], and improve psychological well-being
[156,157]. A meta-analysis of 39 RCTs examined the effects
of exercise on people diagnosed with a mental illness, and found
large effect sizes for depressive symptoms (standardized mean
difference=0.80, 95% CI 0.47-1.13) and schizophrenia
symptoms (standardized mean difference=1.0, 95% CI
0.37-1.64), and a moderate effect size for quality of life
(standardized mean difference=0.64, 95% CI 0.35-0.92) [158].
Effective smartphone apps that promote physical exercise have
already been developed [7], but lack an explicit link to mental
health that mental-health-focused users may need to justify their
use. Motivating MHapp users to engage in physical exercise
can have a broad range of mental health benefits.
Another activity that has been directly linked to mood
improvement is music listening. Music can be a powerful tool
for evoking emotion [159]. Furthermore, relaxing music can
challenge emotional recall biases [160] and decrease anxiety
[161]. Over 68% of users listen to music on their smartphones
[1], and many users use music to reach specific emotional goals
[162,163]. An MHapp that includes music listening activities
could help users with emotional regulation.
Behavioral Activation
Behavioral activation (BA) is a key CBT technique that involves
encouraging individuals to engage in physiologically activating
and psychologically rewarding activities [164]. A meta-analysis
of 17 RCTs reported that BA for clinical depression
outperformed control conditions (standardized mean
difference=0.70, 95% CI 1.00 to 0.39) and was as effective
as CBT-as-usual (standardized mean difference=0.08, 95% CI
0.14 to 0.30) [165]. There is also evidence that BA can help
relieve anxiety [166]. BA aims to (1) encourage the planning
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of activities and the setting of goals so that clients move away
from relying on mood-dependent behaviors; (2) break cycles
of avoidance behavior; and (3) develop skills that focus attention
on the present moment to enable engagement in activities and
associated experiences of pleasure [167]. Motivating MHapp
users to complete BA activities is therefore a simple and
effective way to improve mental health and well-being
outcomes.
Inactivity perpetuates itself via a vicious cycle of low mood:
inactivity can lead to decreased opportunities to experience
pleasure or gain a sense of mastery, which in turn leads to an
increase in negative thinking. This leads to decreased mood,
which again leads to greater inactivity, and so forth [168]. BA
helps to break this cycle by scheduling activities and reducing
escape and avoidance behaviors [167]. Selecting activities that
involve mastery and promote positive feelings of self-worth is
recommended [168], as such activities can boost motivation via
factors related to SDT as well as self-efficacy [100]. Classifying
activities as routine, pleasurable, or necessary can be useful, as
each has different motivations and benefits to performing [169].
To maximize the likelihood that a recommended behavior will
actually be performed by a smartphone user, the behavioral
economics of the situation need to be considered [5].
Using a framework such as Fogg’s [170] behavior model, which
has been specifically designed with app users in mind, can help
in the selection of short, tangible, and universal activities that
will maximize user engagement. Fogg’s behavior model states
that three factors determine the likelihood of a target behavior
occurring: behavior triggers, elements of motivation, and
elements of simplicity. Most relevant to selecting BA activities
are elements of simplicity, which affect a user’s ability to easily
perform the behavior, and include factors such as time, money,
physical effort, mental effort, social deviance, and routine.
Feedback and self-reflection (see the “Reporting of Thoughts,
Feelings, or Behaviors” section) can be an important part of
behavioral activation [169]. An app that promotes reflective
learning by encouraging an activity and then prompting
reflection on the experience immediately after can promote
self-discovery [171].
Coping Skills Training
Coping skills training is the most direct way of improving
self-efficacy [172,173]. Coping self-efficacy (CSE) is a type of
self-efficacy reflecting an individual’s perceived ability to
effectively cope with adversity and distress [174]. Individuals
with high CSE have confidence in their ability to cope with
adversity [175] and engage in more active coping strategies
[176]. Having greater CSE is associated with better mental
health outcomes, including lower likelihoods of depression
[177] and anxiety [174], lower overall psychological distress
[178-180], and greater psychological thriving [181].
Furthermore, CSE can decrease the negative effect of stressful
events on physical health [182]. The greater an individual’s
CSE, the less likely they will also be to avoid anxiety-provoking
situations [174]. Avoidance plays a key role in the development
of anxiety, depression, and many other psychological disorders
[183], so interventions that boost CSE by encouraging
participation in psychologically beneficial activities will both
reduce day-to-day distress and help prevent disorders from
developing.
The development of coping skills is a central component in
CBT-based practices, and such skills can help clients reduce
distress that can trigger problematic maintenance cycles
[54,100,104,184]. For example, a core exercise in the treatment
of anxiety is the development of relaxation skills, and a
meta-analysis of 27 RCTs found a medium to large effect size
for relaxation training on anxiety (d=0.57, 95% CI 0.52-0.68)
[117]. Relaxation training not only develops skills to reduce
physiological arousal, but also builds self-efficacy and
confidence in coping ability [185,186]. CBT for depression also
involves exploration of activities that can reduce distress and
improve self-efficacy [187,188]. Research in positive
psychology stresses that development of a coping skills
repertoire is not only beneficial for those vulnerable to anxiety
or depression, but also important for individuals to function
well emotionally and achieve their full potential [189]. Offering
a range of different strategies and thereby allowing a client to
choose which one fits them best can boost self-efficacy and
perceived control [190,191]. Furthermore, according to SDT,
this choice and control can feed intrinsic motivation toward
self-improvement [70].
Unfortunately, there is currently a lack of technology-based
interventions designed to develop CSE in relation to mental
health. A comparison of 2 Web-based interventions for diabetes
management, one involving coping skills training and the other
focusing on education, showed that although both interventions
had a positive effect on diabetes self-efficacy, only the coping
skills (ie, active) intervention showed significant increases in
primary control coping behaviors and decreases in perceived
stress [192]. Other studies have found no advantage of coping
skills training over educational interventions [193-195], but
none has investigated the impact of the type of real-time
engagement that smartphone apps offer. Many of the coping
skills interventions investigated are limited to a series of
educational sessions about potential coping strategies. By
contrast, smartphone approaches to coping skills interventions
could motivate participants to try a number of different coping
strategies in real-time as they go about their lives and respond
to stressors. This high level of engagement and interactivity
could yield substantial improvements in CSE and psychological
well-being.
Mental Health Information
Psychoeducation, an integral part of CBT, presents clients with
mental health information in an attempt to teach them about the
psychological processes underlying their distress and inform
them of resources available to manage it [196]. A meta-analysis
of 25 RCTs reported that the “Coping with Depression”
psychoeducational intervention, developed by Lewinsohn et al
[197], was effective at treating depression, albeit with a small
effect size (d=0.28, 95% CI 0.18-0.38) [102]. Participants who
completed the preventative version of the intervention were
38% less likely to develop clinical depression [102].
Psychoeducation can also improve mental health outcomes on
a community-wide scale. A meta-analysis of 15 studies
concluded that the Mental Health First Aid program, developed
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by Kitchener and Jorm [198], improved participants’ knowledge
(Glass’s Δ=0.56, 95% CI 0.38-0.74), attitudes (Glass’s Δ=0.28,
95% CI 0.22-0.35), and supportive behaviors (Glass’s Δ=0.25,
95% CI 0.12-0.38) with regard to mental health [199].
MHapps are well positioned to deliver psychoeducation, as they
can engage users with a range of multimedia and audiovisual
tools to aid understanding of mental health concepts. A
meta-analysis of 4 RCTs reported a small effect size (d=0.20,
95% CI 0.01-0.40) for passive psychoeducation including brief
audiovisual sources and information presented via the Internet,
demonstrating that even this minimal form of psychoeducation
is effective at reducing depressive symptoms and psychological
distress [200]. Another meta-analysis of 19 studies found a
significant but small effect size of psychoeducation on stress
(standardized mean difference=0.27, 95% CI 0.14-0.40); in a
follow-up moderator analysis, this study showed that shorter
interventions were significantly more effective than were longer
interventions (P<.05, B=0.020, 95% CI 0.024 to 0.016)
[201]. Smartphones are well equipped to deliver this kind of
brief, passive psychoeducation, and MHapps can offer links to
websites for more in-depth information where required [202].
Psychoeducation topics that have greater relevance to the user’s
reported problems are of greater use to the user, so MHapps
should tailor psychoeducation to individual users (see the
Automated Tailoring” section) [111]. For example, if a user
reports feelings of anxiety, delivery of information about the
physiological responses of anxiety and their relationship with
thoughts and behaviors would be more appropriate than would
delivery of information about the physiological symptoms of
depression. Relevance and engagement may also be enhanced
by adopting a collaboratively empirical approach [64], whereby
users are encouraged to apply concepts learned through
psychoeducation to their own circumstances through hypothesis
testing. An app that engages users in a process of
experimentation-based self-discovery may enhance
psychoeducational outcomes.
Presenting mental health information and engaging individuals
in psychoeducation can lead to boosts in mental health literacy
(MHL) [203]. MHL has been defined as “knowledge and beliefs
about mental disorders which aid their recognition, management
or prevention” [204]. Greater MHL is associated with a
reduction in stigmatizing beliefs about those with mental illness
[205] and with greater and more appropriate help seeking
[144,206,207]. Known factors preventing young people from
seeking help for mental health issues include poor MHL,
preference for self-reliance in problem management, and
perceived stigma of mental illness [77].
Mental health information can also increase treatment
credibility, thereby motivating users to engage with a given
treatment [208], and can provide evidence-based justifications
for performing recommended activities (see the “Recommend
Activities” section). Notably, users have a tendency to perceive
health information on the Internet as being credible [209], so
this raises the ethical imperative of ensuring that all information
is strictly evidence based. Providing links to sources of evidence
may satisfy the needs of scientifically minded users and mental
health experts. The wealth of mental health resources already
available online [210,211] could be utilized by MHapps.
Improving MHL may simply be a case of providing easy access
to these resources through the app.
Christensen et al [212] compared 2 Web-based interventions
aimed at promoting mental health. BluePages, a psychoeducation
site, and MoodGYM, a self-guided CBT site, both led to
decreases in users’depression symptoms. MoodGYM reduced
users’ dysfunctional thinking, whereas BluePages failed to do
this. However, BluePages improved users’ knowledge of
treatments for depression beyond what MoodGYM achieved.
This evidence suggests that both psychoeducation and
self-guided CBT interventions are needed to generate the most
substantial and stable gains in mental health and well-being. A
successful app-based intervention would combine elements of
both psychoeducation and self-guided CBT.
Real-Time Engagement
The high engagement potential of smartphones means that users
are able to seek help for psychological challenges in the moment
they are experiencing them or soon after. MHapps that have not
been designed to be used in real time will fail to capitalize on
valuable opportunities to engage with users.
Many CBT-based therapy programs utilize in vivo exposure
and between-session (homework) activities to help clients
resolve maladaptive anxiety responses in ecologically valid
settings [65,105]. The advantages of between-session
interventions are wide ranging [66] and have already been
covered in this paper under Recommendation 1 “Cognitive
Behavioral Therapy Based.” Some therapy programs have even
utilized virtual reality to harness the power of real-time
engagement [213,214]. These interventions acknowledge the
benefits of engaging with clients in real-world contexts in real
time.
The rationale behind real-time engagement includes basic
behavioral principles of learning. It enhances the generalization
of learned skills to new settings, and can encourage practice of
behaviors to maintain therapeutic gains [215]. Real-time
engagement opens up more opportunities for learning and
applying coping strategies in ecologically valid contexts. Of
the MHapps that aim to increase users’ coping abilities, few
utilize the real-time capabilities of smartphones [8,216]. Most
deliver long-running interventions designed to increase users’
overall resilience or optimism, such as SuperBetter [59]. The
MHapps that do provide users with in vivo coping strategies,
such as MindShift, are very clinically focused, which restricts
their reach (see the “Designed for Nonclinical, Nondiagnostic
Support” section). Engaging users to attempt coping strategies
in real time improves the functionality of the MHapp and
increases opportunities for learning.
Heron and Smyth [217] call health apps that use real-time
engagement “ecological momentary interventions,” and they
present evidence for the efficacy of such apps in psychosocial
applications. Depp et al [110] developed and trialed a mobile
intervention called PRISM that used real-time data to prompt
individuals with bipolar disorder to engage in self-management
behaviors. The results from this study were promising, but this
rather clinically focused intervention was built for PDAs rather
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than for smartphones, and therefore was unlikely to be as
unobtrusive in daily life as smartphone interventions.
Activities Explicitly Linked to Specific Reported Mood
Problems
Linking recommended activities to specific psychological
challenges helps trigger engagement with an intervention. Eyal
[3] emphasizes the need for successful apps to have triggers
that fulfill an immediate and obvious need, using the metaphor
of vitamins and painkillers. Vitamin-like products do not satisfy
immediate needs but are espoused as beneficial, whereas
painkiller-like products give users immediate benefits. MHapps
like SuperBetter [59] and Happify [218] require users to engage
with the app regularly and encourage them to do so by reminding
them of the benefits offered by the app. However, the activities
recommended by these apps are not directly linked to any
specific mood problems that users may be experiencing. Using
specific problems as triggers can strengthen engagement [3]
and can help in the learning of targeted coping strategies.
Utilizing habit formation can be a very effective way of
guaranteeing repeated engagement with an app, which in the
case of MHapps, should lead to mental health benefits. Habits
are repeated behaviors that are triggered by cues [5]. To generate
a habit that involves using an MHapp, a cue must be selected
to associate with app use through the processes of conditioning
[3]. Using mood problems as cues can drive real-time
engagement (see the “Real-time Engagement” section). For
example, an MHapp that is designed to be used when a user is
feeling low or anxious is better suited to habit formation
processes than is an MHapp that offers no cues for engagement
and expects users to engage with it randomly throughout the
day. Habit formation will also be driven if an MHapp is linked
to activities that decrease psychological distress, increase
self-efficacy, or reward users in some other way [5].
Encourage Nontechnology-Based Activities
When designing interventions for smartphones, it may be
tempting to build the therapeutic activities into the app’s
interface. However, this goes against the ethos of CBT-based
practice, which emphasizes the important role of activities and
interventions outside of contact with a practitioner, computer
program, or self-help guide [120]. Encouraging users to engage
in real-world activities, off the device they are using, respects
that ethos and fosters the environmentally valid application of
skills.
In this context, it is also of note that depression and lower
psychological well-being are correlated with Internet use,
especially among introverts with low levels of social support
[219]. However, this role is moderated by the function of
Internet use—for instance, Internet use for communication has
been found to be related to lower levels of depression, whereas
Internet use for noncommunication purposes has been found to
be related to greater depression and social anxiety symptoms
[220]. Internet use and Internet addiction have also been
associated with social anxiety [221], and positive correlations
have been found between avoidance coping and Internet use
[222,223]. This may also apply to Internet-enabled,
noncommunication-based mobile phone apps that distract users’
attention away from psychological challenges. Avoidance coping
has been shown to increase the likelihood of acute and chronic
life stressors and depressive symptoms over long periods [224].
Providing users with nontechnology-based activities helps to
balance MHapp-based technology use with positive behavior
change strategies and limits use of avoidance coping strategies.
Technology can allow greater multimodal learning by combining
text with spoken language, sounds, and graphics that are closer
representations of learning in an applied setting [225]. For
example, blended learning, which involves blending the use of
technology with applied learning in the classroom [226,227],
has been shown to deliver superior learning outcomes to
traditional teaching methods [228,229]. It has been
recommended that technology be used to enhance real-life
experiences, not replace them [230,231]. MHapps may therefore
harness the power of blended, multimodal methods to effectively
enhance learning of real-world coping strategies.
Some available MHapps encourage users to engage in
nontechnology-based activities. SuperBetter motivates users to
engage in regular nontechnology-based resilience-building
activities [232]. Preliminary results from an RCT suggest that
SuperBetter is effective for reducing symptoms of depression
[233]: specifically, SuperBetter users experienced a reduction
in the equivalent of 5 symptoms of depression, and waitlist
participants experienced a reduction in just 2.
Gamification and Intrinsic Motivation to Engage
The therapist plays an instrumental role in promoting clients’
motivation to engage in psychotherapy and undertake homework
activities [65]. This means that self-help CBT may be of limited
use if the user suffers from low motivation and volition, which
is common among those with mood disorders [234].
Gamification is a novel solution that may help counteract
problems with motivation and yield additional well-being
outcomes.
To “gamify” something does not mean to turn it into a digital
game. Gamification is instead the use of “game-based
mechanics, aesthetics, and game thinking to engage people,
motivate action, promote learning, and solve problems” [17].
Many apps have employed the principles of gamification to
motivate users to pursue various goals, but such goals are likely
to be most motivating if they originate from the users themselves
[235]. Gamification can enhance a user’s motivation to pursue
an existing goal, but it does not, in itself, create new goals for
users. These goals may require the formation of new routines,
and gamification excels at motivating people to repeat tasks
until new habits are formed [3]. Some examples include Nike+
Running [236] and other fitness tracking apps that award points
for reaching fitness goals, and Smiling Mind [60], which tallies
minutes spent meditating and awards badges for specific
meditation-related achievements, as seen in Figure 4.
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Figure 4. Screenshot of Smiling Mind displaying achievements.
Games are abstracted, simplified versions of reality, so
gamification can help users reduce reality’s complexity into a
more easily understood operating model [17]. This helps users
to quickly learn cause-and-effect inferences, without complex
extraneous factors detracting from their motivation to make
change. Gamification is also based on the principle that making
something goal oriented can increase the positive feelings
associated with it and drive intrinsic motivation [232]. In this
context, gamification is an applied expression of the concepts
proposed by SDT [17] (see the “Cognitive Behavioral Therapy
Based” section).
Gamification is a means of making intrinsic rewards more
obvious and tangible. Alternate reality games (ARGs) link online
or app-based events and achievements to real-world ones [237].
By tracking and quantifying the progress of real-world goals,
users are able to reflect on their competency and experience
mastery. Gamification also helps to break larger, more abstract
goals down into smaller, more tangible and concrete tasks. For
example, if a user’s goal is to build resilience and recover from
depression, the MHapp and ARG SuperBetter is able to break
that goal down into daily tasks of activating 3 power-ups,
battling 1 bad guy, and doing 3 quests [59]. Although many
regular electronic games are attractive because they are escapist
[238], ARGs are antiescapist, motivating users to deal with
real-world challenges and increasing the likelihood of them
obtaining intrinsic rewards.
Individuals tend to choose more challenging activities when
these activities are framed as games and imbued with intrinsic
motivation [239,240], and making activities goal-directed further
enhances enjoyment of their challenges [241]. When building
points and award systems for gamified solutions, it is best to
introduce users by awarding them some points or rewards on
sign-up or early on. The endowed progress effect means that
starting with some points rather than zero increases effort and
motivation to engage [242].
Although fun is the primary reward in electronic games,
self-efficacy is the primary reward in well-structured gamified
solutions [235]. Gamification principles can amplify
achievements by offering immediate reflections of intrinsic
rewards, thereby boosting self-efficacy. Badges, points, and
other gamification rewards remind users that they have achieved
something by quantifying their success and allowing users to
reflect on their own growth [232]. Even apparent failure can be
rewarding in a gamified environment, if the right animation or
interaction—namely, one that maintains the user’s feelings of
competency—is used [17].
One study found that the reward- and motivation-related
neurotransmitter dopamine was released during a simple,
goal-directed game-based task, presenting neurological evidence
for why game-based mechanics may yield positive well-being
effects [243]. A meta-analysis of 10 RCTs found that
electronic-game-based depression interventions had a moderate
effect on depressive symptoms (d=0.47, 95% CI 0.69 to
0.24) [244].
Apps allow constant improvement through updates and
Web-delivered content [245], and this is very important for a
successful gamified solution. Not only should the gamified
structures be tweaked until users are being optimally engaged,
but also novel and untried features should be introduced to
motivate users to maintain their engagement with the app. Apps
that sustain variability throughout use can maintain user interest
with the promise of new and interesting content [3].
Log of Past App Use
Gamification relies on users having the ability to record and
review their achievements. Thus, having a well-presented log
of past app use can potentially raise intrinsic motivation and
increase users’investment in the app. Logs of past use can also
enable automated tailoring (see the “Automated Tailoring”
section). If a log is being recorded for this purpose, then making
it accessible to the user should not present coding difficulties.
Narratives in games can link discrete, seemingly unrelated tasks
[232]. Narrative framework embedded into an app’s use can
motivate users to do small tasks to work toward an overall goal.
Using a log that provides users with useful feedback about their
successes and challenges can provide this narrative framework.
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For example, many mental health boosting activities, such as
exercise, relaxation, and cognitive reframing, appear to be
unrelated. However, embedding them into a narrative that has
an end goal related to boosting mental health can help users
make sense of the tasks, thereby boosting users’ motivation to
achieve these goals.
Wilson’s [246] story-editing technique can be applied to apps
to enhance engagement [5]. According to Wilson’s theory,
reinterpretation of a self-narrative can affect future behavior.
Past failings can be reinterpreted as learning opportunities, and
other actions can be framed as preparations for a specific goal.
Altering self-narrative in this way helps users see “themselves
as someone for whom the action is a natural, normal extension
of who they are” [5]. For example, fitness trackers and apps
that count a user’s steps, such as the Jawbone UP [247], show
users that they have already been exercising, but may need to
increase their level slightly to achieve their goals.
The addition of more storyline-based game principles, such as
avatars with experience points, can further reinforce a sense of
narrative [17]. Avatars are characters within a game that are
representations of the user [248]. Bandura’s [249] social
cognitive theory states that the relatability and similarity of a
model will increase the likelihood of a learned behavior being
performed. Fox and Bailenson [250] substantiated this in a
digital environment, with participants exercising more when
they were shown an exercising avatar that resembled them than
when the avatar did not resemble them. Furthermore, users who
are given taller avatars act more confidently and aggressively
than do those who are given shorter avatars, both virtually and
face-to-face [251,252]. This indicates that the narrative elements
used in a gamified solution can translate to behavioral changes
in the real world. If users are capable of exercising autonomy
and customizing their avatars so that these avatars better
resemble users' ideal state, the likelihood of behavioral
modification should be improved.
Importantly, users must also be aware of the cognitive or
behavioral work they have completed. Investment through labor
and work increases engagement and enjoyment [253].
Understood through SDT, this may be a reflection of a user’s
desire to build competency and mastery [254]. Therefore, users
who can log the extent of their app use and receive feedback
on how much they have done or invested are more likely to
have greater, more enjoyable engagement with the app.
To maintain a log of app-based activity, users may have to create
an account to synchronize their app progress with a server. This
would allow users to use multiple devices and help them avoid
losing their progress if their app were deleted or they changed
devices. Many apps use a social networking site login, such as
Facebook, for easy account creation, but this can trigger
privacy-related anxieties in users [255], so it may be best to
avoid this when creating an MHapp that collects potentially
sensitive data. Other ethical and privacy concerns arise when
recording app data to a server [256], so the integrity of storage
sites should be thoroughly evaluated, especially with regard to
obtaining users’informed permission to record and access their
personal data [116].
Reminders to Engage
Some of the most successful guided self-help Web-based
treatments for anxiety and depression use email or telephone
reminders to maintain user engagement [10]. Reminders can
increase adherence and reduce dropout from self-help CBT
interventions [24]. Push notifications are alerts that can be sent
via the Internet to apps on mobile devices [257]. MHapps that
use push notifications are similar to Internet interventions that
use short message service (SMS) reminders in that they prompt
users throughout their day to engage in the intervention.
Previous studies have demonstrated that interventions with SMS
reminders can be effective for diabetes management [258],
smoking cessation [259], and weight loss [260].
Although external triggers can be useful to remind users of an
app, too many annoying or interruptive reminders can lead to
disengagement. SDT stipulates that anything that quashes a
sense of autonomy, such as a series of insistent reminders, can
reduce intrinsic motivation to engage [71]. Eyal [3] distinguishes
internal and external triggers of engagement, extoling the
long-term benefits of the former over the latter. External triggers
may help to initiate the engagement processes, but internal
triggers are more reliable drivers of long-term habits. Eyal cites
the example of social image-sharing app Instagram, which uses
the internal trigger “I want to share this experience with others.
However, if Instagram reminded users every day to post an
image, it is likely that using it would soon be perceived as a
chore with no intrinsic reward.
Although some reminders can restrict a sense of autonomy,
others can encourage it. A recent meta-analysis of 42 studies
found that phrases that emphasize an individual’s right to refuse,
such as “But you are free to accept or refuse,increase the
likelihood of people agreeing to requests, with an overall effect
size of r=0.13 [261]. External reminders should be framed within
an SDT context to grant autonomy and respect intrinsic
motivators. Chaiken’s [262] heuristic-systematic processing
theory can further inform the design of reminder
communications. Framing reminders to satisfy the commitment
and consistency, liking, authority, or scarcity heuristics can aid
user engagement [263].
Simple and Intuitive Interface and Interactions
The simplicity of a program’s interface and ease of navigation
significantly influence user perceptions of quality in Web-based
mental health interventions [264,265]. User satisfaction and
perceptions of credibility directly influence engagement and
therapeutic benefit [208]. Building an enjoyable app with good
graphic design and a slick, intuitive, and satisfying interface is
necessary for an effective intervention [5,266]. Simplicity also
reduces the likelihood of technical difficulties that may dissuade
users from engaging [267].
Fogg’s behavior model (ie, the model of technology-based
behavior change [268] discussed in the “Recommend Activities”
section) emphasizes that simplicity reduces demands for
initiating behavior outcomes, and increases the likelihood of a
behavior occurring. A simpler interface decreases the ability
required to engage with the app, and increases the likelihood
of successful engagement [3].
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No-action default (or “opt out”) options have enormous
influence over the use of a product or service [269]. For
example, countries that have presumed consent organ donation
policies have 25-30% higher donation rates after all other factors
that influence rates are accounted for [270]. It has been argued
that making organ donation as the no-action default option for
Australian citizens could significantly raise donation rates and
save many lives [271]. No-action defaults both preserve
autonomous decision-making and influence behavior toward
goals [272], so MHapps are well positioned to capitalize on
these effects to guide users toward beneficial outcomes. App
settings should be customizable to allow for autonomous use
and tailoring, but come with recommended default options
preset. For example, the default option for reminders should be
set to “on,” and at a frequency that is not overwhelming for the
user (see the “Reminders to Engage and External Triggers”
section).
The language used in the delivery of a mental health
intervention, particularly a self-help intervention, can also have
a major impact on engagement [273]. The language needs to
be simple, concrete, confident, and hopeful for users to
understand and engage with interventions. Language should
also be inclusive of all sexual orientations and lifestyles [274]
and be nonclinical, nonpsychopathological, and nondiagnostic
to avoid stigma [57,99]. The literacy of intended users must be
considered, just as it is for different newspapers [275]. The
length of sentences and paragraphs is not only limited by the
constraints of a smartphone screen, but also by the working
memory of users. Making information meaningful to users can
help its consolidation into memorable chunks, easing the
demands on memory [276]. Using illustrations, such as faces,
for emotions, can also improve the efficiency of understanding
[277]. Decreasing load on memory is all the more important for
users suffering from symptoms of depression or anxiety, which
can restrict working memory function [278].
Although keeping information simple is necessary for initial
understanding, enabling exploration of more in-depth
information is important to satisfy some users [202]. Building
a feature such as a “learn more” or “help” button into an MHapp
can enable users to access more information about certain
content or features. Furthermore, navigation around an app can
be key to maintaining a sense of autonomy and competency.
An app that limits a user’s freedom of navigation may be
frustrating and not intrinsically rewarding to use. Features such
as an ever-present button that navigates the user back to the
home screen can remedy this.
Links to Crisis Support Services
Crisis support services are valuable resources for vulnerable
individuals undergoing acute psychological distress [279].
Suicidal callers to crisis hotlines experience significant decreases
in suicidality, hopelessness, and psychological pain [280].
Developing and utilizing these services has consequently
become a key area for promoting public mental health care
[281,282]. However, barriers to help seeking can prevent
troubled individuals from utilizing these supports.
Building links to crisis support services into MHapps may
overcome some of these barriers. Furthermore, an MHapp that
records a user’s mood (see the “Reporting of Thoughts, Feelings,
or Behaviors” section) may be able to unobtrusively detect
indicators of depressive episodes and prompt contact of the
relevant supports. Negative attitudes toward seeking help can
be a major barrier to engagement [77]. However, if an app
presents support options in an attractive and easy-to-access way,
accessing those supports is more likely to be perceived as
acceptable and appealing [269]. Lack of awareness of service
availability, or the nature of support offered, can also prevent
help seeking [203], as can the belief that support is rarely
available and will not help anyway [283]. An MHapp that
enables access to information about how support services
operate and how they can help could reduce these barriers.
According to the Fogg’s behavior model [268], accessing crisis
support services through technology should be made
straightforward to reduce barriers to action and increase the
likelihood of service contact being made.
Importantly, Internet supports are preferred to telephone
helplines in some populations, including young people [284].
Organizations such as Lifeline have an online crisis support
chat facility [285], so where these are available, links should
be offered on mobile devices. There is also growing support for
the effectiveness of online chat options [286], which may be
better suited to how some individuals who use digital devices
tend to communicate [287].
Experimental Trials to Establish Efficacy
A major shortcoming of currently available MHapps is the lack
of RCT evidence for their efficacy. Although many apps use
evidence-based frameworks, like CBT, only a handful have
been experimentally trialed. Donker et al [8] conducted a
systematic review of the literature, searching for evidence of
effective MHapps; only 8 papers were identified as providing
scientific support for MHapps, and in these papers, only 5
separate MHapps were described. Just 1 of these 5 was a
self-contained app, with the other 4 requiring input from a
mental health professional. Frustratingly for those who might
benefit from these apps, none of them is currently available on
the iOS or Android app stores.
This lack of controlled outcome research in the field is
unexpected, given the ease of collecting data using mobile and
Internet technologies [90]. Although validation of other
psychological interventions requires time-consuming
assessments, MHapps are capable of reliably, quickly, and
automatically collecting a myriad of self-report and behavioral
usage data [288].
When starting with a product vision for an app, target outcomes
should be well defined in concrete, objective, and measurable
terms [5]. These overarching goals guide development and
enable a definition of success for the app. There are three main
types of data that can be used to assess the target outcomes of
MHapps: (1) assessment tools administered before and after a
set period of app use, (2) EMA techniques to administer multiple
brief self-report questionnaires throughout app use, and (3) app
usage data. A thorough assessment of an MHapp should attempt
to use all three data sources.
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Assessment Tools Administered Before and After a Set
Period of App Use
Wendel [5] stresses that, where possible, target outcomes for
apps should avoid user “states of mind,” such as emotions and
other internal, psychological variables, as these are problematic
to measure. However, the main goal of MHapps is to alter the
user’s state of mind. This means the tools used to measure the
MHapp’s target outcomes should be selected carefully, keeping
in mind the ease of administration via a smartphone, the ease
of integration into an MHapp’s interface, the licensing of the
assessment tool, and the validity and reliability of the measure.
Outcome measures for MHapps should contain a suitable
assessment of emotional well-being and mental health. For
example, the 9-item Patient Health Questionnaire (PHQ-9) [289]
is a brief, self-administered, valid, and reliable measure with
88% specificity and 88% sensitivity for major depression. It is
licensed to be used freely, and existing apps have successfully
adapted it for a smartphone interface [290]. The 7-item
Generalized Anxiety Disorder scale (GAD-7) [291] is a similar
measure for anxiety, and using both the PHQ-9 and GAD-7
together can give a balanced assessment of emotional
psychopathology [292]. To assess the languishing-flourishing
dimension of mental health, the 14-Likert-item
Warwick-Edinburgh Mental Well-Being Scale could be used,
as it is a brief, reliable, and valid tool [293].
Secondary to mental health outcome measures are measures of
the MHapp’s intervention targets. For example, a
self-monitoring MHapp should aim to assess the degree to which
insight and ESA are being enhanced by the self-monitoring
intervention (see the “Reporting of Thoughts, Feelings, or
Behaviors” section). To validate their MHapp, Kauer et al [133]
used a short survey, delivered by phone, called the ESA Scale.
This tool comprises 33 items, all rated on a scale from 0 (never)
to 4 (a lot), and was adapted from the 20-item Self-Reflection
and Insight Scale [294], the 10-item Ruminative Response Scale
[295], and the 12-item Meta-Evaluation Scale [296]. MHapps
that aim to boost CSE (see the “Recommend Activities” section)
could use the Coping Self-Efficacy Scale [175], which is a short
questionnaire that can be administered via a smartphone.
MHapps that utilize elements of psychoeducation may require
assessments of MHL (see the “Mental Health Information”
section). There is no standardized assessment tool for MHL,
but it is often measured using self-report questionnaires and
vignettes [204], which can be adapted for smartphone-based
assessment. However, vignettes tend to be long and cumbersome
forms of assessment, and are not well-suited to the restrictions
of smartphone screens and interfaces. A well-validated,
standardized, brief assessment tool for MHL would benefit the
development of many self-help interventions, including
MHapps.
It is recommended that follow-up data are collected at several
different time points throughout the MHapp intervention and
after its use has been concluded. An RCT on the mindfulness
meditation app Headspace [297] found that it led to increases
in positive affect and decreases in depression, but had no effects
for measures of negative affect, satisfaction with life, or
flourishing. This failure to uncover effects may be attributable
to the limited time course of the research, as the intervention
only lasted for 10 days and there was only one postintervention
measurement [298].
Ecological Momentary Assessment
Using EMA, brief self-report questionnaires can be prompted
at various periods throughout a user’s day [143], with the precise
time of survey completion accurately recorded. EMA can reduce
bias in self-report data [142] and enables study of ecologically
valid contexts [141]. As described in the “Reporting of
Thoughts, Feelings, or Behaviors” section, EMA can also be a
valuable part of interventions.
It is important to adopt an EMA design that is most appropriate
for the types of data being collected and for the MHapp being
trialed. EMA questionnaires should be brief enough for
smartphone users to feel capable of completing them without
too much interruption to their day. The aim of EMA is to obtain
an ecologically valid measurement, so limiting disruption
maximizes validity [217]. The design of EMAs can be
event-based or time-based, depending on whether responses are
collected following a specific event, such as an app-based
interaction, or triggered at a given time point [141]. The choice
in design should also be well thought-out and justified. For
example, if a time-based EMA collects measurements at the
exact same time every day, it may not accurately capture
changes in the user’s state experienced throughout the rest of
the day. Event-based EMA should be used in an MHapp that
recommends activities (see the “Recommend Activities” section)
and requests a user to rate their mood before and after
performance of the activity (see the “Reporting of Thoughts,
Feelings, or Behaviors” section).
App Usage Data
Ongoing monitoring of client data is valuable to the validation
of CBT-based interventions [142], and ongoing data collection
should be a seamless and constant background process on
smartphone apps. App usage data are often collected
continuously by app developers to analyze user behavior and
improve app functionality. The range of data capable of being
collected in this way is very large, including measurements such
as time spent using specific features of an app, number of times
the app is used in day, and what times in the day features on
the app are being used.
Data collected via EMA and other assessment tools may also
provide insight into user variables that affect patterns of app
usage. For example, it may be found that a specific feature is
used most when users are highly distressed. This is an important
information to consider, for both the development of
psychological theories and the development of MHapps, as it
may be appropriate to display a link to crisis services on the
app’s interface when a specific feature is being used.
Program adherence is easily assessed with usage data, and app
design can be concurrently altered to increase adherence [24].
Although there is no doubt that these data are already being
used by developers to improve individual MHapps, there has
seemingly been a lack of academic transparency to validate
those MHapps and aid in the development of others.
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Strength of Evidence for Recommendations
Each recommendation explored in this review is supported by
a different rank of evidence. Table 2 summarizes the 16
recommendations and ranks each according to evidence strength.
The strongest level listed includes recommendations that are
demonstrably effective, as shown by the numerous
meta-analyses and RCTs of interventions previously cited in
this review. However, more research in the form of RCTs is
needed for such MHapps. The next rank of evidence pertains
to recommendations that are probably effective according to
available evidence but still require more research in the MHapp
field. The rank under this includes recommendations that appear
to be promising according to the evidence, but, again, must be
researched in more depth to validate their stated principles in
self-help interventions, including MHapps.
Discussion
MHapps offer exciting new opportunities to improve and
manage the mental health of smartphone users. This review has
generated 16 recommendations to be considered in the
development of future MHapps. In summary, MHapps should
aim to prevent emotional mental health problems by employing
a wide array of CBT-based techniques that are tailored to an
individual’s needs and delivered via a simple, interactive design.
Structures of gamification and habit formation should be used
to maximize engagement in the app’s interventions. The app
itself should be experimentally validated, and user data should
be utilized for its ongoing improvement.
It is highly recommended that MHapp developers familiarize
themselves with the literature, both in the field of self-help CBT
and in the field of app-based behavior change, before embarking
on any MHapp projects. Respecting the value of both of these
research fields should enable the reliable, engaging delivery of
an evidence-based mental health intervention. This review may
help developers get started with this familiarization process,
but further reading is strongly advised. Furthermore, a
multidisciplinary team consisting of experts in app usability
engineering, programming, data collection and analysis, industry
and health care sector applications, clinical psychological
interventions, and any other relevant fields is strongly advisable.
The Mobile Application Rating Scale (MARS) is a recently
developed measure enabling objective, multidimensional rating
and comparison of mobile health apps [299]. Tools such as this
will be essential for the future of MHapp development, and will
enable clinicians and consumers to make more informed
decisions about their choice of smartphone-based support.
There is a risk of researchers developing MHapps primarily for
research needs rather than to meet the needs of end users. When
an MHapp is released to the public, it is a self-contained product
and must operate efficiently in the user’s daily routine. For
MHapp research to be ecologically valid, MHapp developers
must create self-contained apps that still function outside of a
research setting. Several RCTs have been conducted on MHapps
that are not publically available [52]. This prevents researchers
and intervention developers from analyzing and exploring
existing evidence-based MHapps. It also blocks help seekers
from finding evidence-based MHapps and benefiting from
effective support.
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Table 2. Recommendations for future mental health apps.
DetailsRecommendationEvidence
Start with an evidence-based framework to maximize effectiveness1. Cognitive behavioral therapy basedDemonstrably effective, but
more research needed in
MHapp field Increases accessibility and addresses comorbidity between anxiety
and depression. Also compatible with transdiagnostic theories of
anxiety and depression
2. Address both anxiety and low mood
Avoiding diagnostic labels reduces stigma, increases accessibility,
and enables preventative use
3. Designed for use by nonclinical populationsProbably effective, but more
research needed in MHapp
field Tailored interventions are more efficacious than is rigid self-help4. Automated tailoring
Self-monitoring and self-reflection to promote psychological growth
and enable progress evaluation
5. Reporting of thoughts, feelings, or behaviors
Behavioral activation to boost self-efficacy and repertoire of coping
skills
6. Recommend activities
Develop mental health literacy7. Mental health information
Allows users to use in moments in which they are experiencing
distress for optimum benefits of coping behaviors and relaxation
techniques
8. Real-time engagement
Enhances understanding of cause-and-effect relationship between
actions and emotions
9. Activities explicitly linked to specific reported
mood problems
Supported by theory and in-
direct evidence but focused
research needed Helps to avoid potential problems with attention, increase opportu-
nities for mindfulness, and limit time spent on devices
10. Encourage nontechnology-based activities
Encourage use of the app via rewards and internal triggers, and
positive reinforcement and behavioral conditioning. Also links with
flourishing
11. Gamification and intrinsic motivation to en-
gage
Encourage use of the app through personal investment. Internal
triggers for repeated engagement
12. Log of past app use
External triggers for engagement13. Reminders to engage
Reduce confusion and disengagement in users14. Simple and intuitive interface and interactions
Helps users who are in crisis to seek help15. Links to crisis support services
It is important to establish the app’s own efficacy before recommend-
ing it as an effective intervention
16. Experimental trials to establish efficacyNecessary for validation of
principles
A behavioral plan is a “detailed ‘story’ of how the user
progresses from being a neophyte to accomplishing the action
while using the product” [5]. Any app should be designed from
the foundation of a comprehensive behavioral plan [5]. This
means that it may not be possible to incorporate all 16
recommendations listed herein into a single MHapp. To guide
development of behavioral plans and interactive frameworks,
it would be helpful to focus on specific foundations. Three of
the recommendations listed can be used as foundations for
intervention development, as they aim to target specific
psychological constructs, such as ESA, MHL, and CSE. The
“Reporting of Thoughts, Feelings, or Behaviors” section details
mood reporting, self-monitoring, and improving ESA. MHapps
that use this as a foundation could be referred to as
“reflection-focused.” The “Recommend Activities” section
relates to engaging users in activities to improve their CSE.
MHapps that use this as a foundation could be referred to as
“goal-focused.” The “Mental Health Information” section relates
to mental health information, psychoeducation, and improving
MHL. MHapps that use this as a foundation could be referred
to as “education-focused.” More research is needed to
investigate the different effects of reflection-focused,
education-focused, and goal-focused MHapp designs on mental
health, and whether different users obtain different benefits
from each design.
Each recommendation explored in this review could be the
target of an RCT. RCTs that compare identical MHapps with
or without specific features could provide evidence for or against
these features in future MHapps. However, it is important to
acknowledge the influence of the overall behavioral plan on the
MHapp’s effectiveness. Some features may work better in one
MHapp’s behavioral plan than in another’s, and simply including
more recommended features may not improve the overall
intervention. Future MHapp and eHealth RCTs should aim to
validate underlying theories and principles for intervention
improvement [21].
The World Health Organization [300] predicts that depression
will become the global leading cause of disease burden by 2030.
There is an enormous worldwide need for better preventative
mental health, and MHapps that target emotional well-being
are set to provide exciting new opportunities in the field. The
evidence-based recommendations discussed herein are important
for all MHapp developers to acknowledge if better interventions
are to be developed to meet this rising demand in the future.
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Conflicts of Interest
None declared.
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