ArticlePDF Available

A Systematic Review of Cognitive Behavioral Therapy and Behavioral Activation Apps for Depression

Authors:

Abstract and Figures

Depression is a common mental health condition for which many mobile apps aim to provide support. This review aims to identify self-help apps available exclusively for people with depression and evaluate those that offer cognitive behavioural therapy (CBT) or behavioural activation (BA). One hundred and seventeen apps have been identified after searching both the scientific literature and the commercial market. 10.26% (n = 12) of these apps identified through our search offer support that seems to be consistent with evidence-based principles of CBT or BA. Taking into account the non existence of effectiveness/efficacy studies, and the low level of adherence to the core ingredients of the CBT/BA models, the utility of these CBT/BA apps are questionable. The usability of reviewed apps is highly variable and they rarely are accompanied by explicit privacy or safety policies. Despite the growing public demand, there is a concerning lack of appropiate CBT or BA apps, especially from a clinical and legal point of view. The application of superior scientific, technological, and legal knowledge is needed to improve the development, testing, and accessibility of apps for people with depression.
Content may be subject to copyright.
RESEARCH ARTICLE
A Systematic Review of Cognitive Behavioral
Therapy and Behavioral Activation Apps for
Depression
Anna Huguet
1,2
*, Sanjay Rao
3,4
, Patrick J. McGrath
1,5,6
, Lori Wozney
1
, Mike Wheaton
1
,
Jill Conrod
1
, Sharlene Rozario
1
1Center for Research in Family Health, IWK Health Centre, Halifax, Nova Scotia, Canada, 2Department of
Community Health & Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada, 3Annapolis Valley
Health, Kentville, Nova Scotia, Canada, 4Department of Psychiatry, Dalhousie University, Halifax, Nova
Scotia, Canada, 5Departments of Pediatrics and Science, Dalhousie University, Halifax, Nova Scotia,
Canada, 6Nova Scotia Health Authority, Halifax, Nova Scotia, Canada
These authors contributed equally to this work.
*anna.huguet@iwk.nshealth.ca
Abstract
Depression is a common mental health condition for which many mobile apps aim to pro-
vide support. This review aims to identify self-help apps available exclusively for people
with depression and evaluate those that offer cognitive behavioural therapy (CBT) or beha-
vioural activation (BA). One hundred and seventeen apps have been identified after search-
ing both the scientific literature and the commercial market. 10.26% (n = 12) of these apps
identified through our search offer support that seems to be consistent with evidence-based
principles of CBT or BA. Taking into account the non existence of effectiveness/efficacy
studies, and the low level of adherence to the core ingredients of the CBT/BA models, the
utility of these CBT/BA apps are questionable. The usability of reviewed apps is highly vari-
able and they rarely are accompanied by explicit privacy or safety policies. Despite the
growing public demand, there is a concerning lack of appropiate CBT or BA apps, especially
from a clinical and legal point of view. The application of superior scientific, technological,
and legal knowledge is needed to improve the development, testing, and accessibility of
apps for people with depression.
Introduction
Depression is one of the most common mental health disorders [1] which often begins in ado-
lescence and if left untreated, may persist into adulthood [2]. It ranks 4th in the global burden
of disease [3]and is of significant economic cost to society[4]. Cognitive Behavioural Therapy
(CBT) and Behavioural Activation (BA) are now an accepted evidence-based first-line treat-
ment for depression [5]. Both CBT and BA have meta-analytic level of evidence in the treat-
ment of depression[6,7]. Periodic face-to-face sessions between therapist and patient have been
the most traditional medium to deliver CBT and BA. However, with population estimates of
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 1/19
a11111
OPEN ACCESS
Citation: Huguet A, Rao S, McGrath PJ, Wozney L,
Wheaton M, Conrod J, et al. (2016) A Systematic
Review of Cognitive Behavioral Therapy and
Behavioral Activation Apps for Depression. PLoS
ONE 11(5): e0154248. doi:10.1371/journal.
pone.0154248
Editor: Kim-Kwang Raymond Choo, University of
South Australia, AUSTRALIA
Received: September 30, 2015
Accepted: April 11, 2016
Published: May 2, 2016
Copyright: © 2016 Huguet et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information files.
Funding: The authors have no support or funding to
report.
Competing Interests: The authors have declared
that no competing interests exist.
Major Depression at 6.7% and even higher for Non-Major Depression [1], it is unlikely that
this traditional approach can reach everyone.
More recent research indicates that depression can be treated successfully with CBT and BA
based self-help interventions delivered over the Internet [8,9,10]. This type of therapy is suited
for digital delivery as demonstrated by the fact that there are more Internet-based studies on
CBT/BA than on other evidence-based models (e.g., Interpersonal Therapy or Acceptance and
Commitment Therpy). There is a strong case in healthcare for addressing access to CBT or BA
through the use of technology, with mobile applications (apps) being one possible means of
delivery. Apps could be especially useful in early treatment of depression in young people who
report high levels of smartphone device use [11].
Smartphone use is a growing phenomenon [12] and has the advantage of being accessible,
mobile, and easy to operate, with decreasing cost of use. Smartphones have been used to facili-
tate the delivery of healthcare interventions including treatment of mental health conditions
[13]. The number of apps intended to help people cope with depression is increasing rapidly,
especially in the commercial marketplace [14,15]; however the development process, usability,
feasibility, and efficacy of these apps developed in the commercial marketplace are rarely
assessed or reported. The quality of the available apps has not been the subject of any system-
atic reviews, until now.
It is vital to perform a systematic review of apps for depression to identify what currently
available apps are based on strong and recommended evidence models for depression. Evaluat-
ing the available apps can inform future development of effective smartphone delivered inter-
vention for depression. The purpose of this systematic review was twofold: (1) To identify all
currently-available native apps that provide information, support or treatment for depression;
(2) To evaluate CBT or BA self-help (either guided or unguided) apps on their usefulness,
usability, and integration and infrastructure, as recommended by Chan et al. [16]. Usefulness
was determined by evaluating how accurately each CBT/BA app tapped into the core of the
CBT and BA models, and by exploring whether the efficacy or effectiveness of the CBT/BA
apps have been proven or not. Usability was evaluated by comparing each CBT/BA app to a list
of heuristics, and integration and infrastructure was evaluated by looking whether the CBT/BA
apps included a privacy policy and addressed safety issues.
The results of this review can assist care providers in choosing appropriate apps for the
treatment or research of depression. The review will also identify areas for future development
to effectively provide CBT or BA for depression through smartphones.
Methods
Inclusion and Exclusion criteria
We included in our review those apps that met the following inclusion criteria: (1) the app
description stated that they provide treatment or support for depression as its exclusive goal;
(2) the app was publically available for download within Canada at the time this review was
performed (December, 2015), and consequently also fully available for evaluation by the
research team; (3) the app was defined as a native app (i.e., developed for one particular mobile
device and installed directly onto the device itself) compatible with smartphones. We excluded
from the review those apps which specifically addressed depressed subpopulations (e.g.,
depressed people with diabetes, postpartum depression) because they have special health care
needs that require different care. We also excluded those apps that were designed to support
health care professionals working with depressed populations because these apps are addressed
to a different audience. We excluded web-based/Internet-enabled apps only accessible via the
Systematic Review of CBT and BA Apps for Depression
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 2/19
mobile devices Web browser because they are very challenging to identify in a systematic way.
Finally, we also excluded those apps which were only available in a non-English language.
Search strategy
The apps included in this review were identified by searching both the scientific literature and
commercial marketplace.
The search of the scientific literature. The following databases from health sciences and
computer science were searched: IEEE, ACM Digital Library, EMBASE, PubMed (Medline),
PsychINFO, and Web of Science. A library information specialist created the database-specific
search strategies by combining population-specific term (i.e., depression) and terms related to
technical delivery (i.e., app, smartphone, mobile phone, cell phone, text message, iphone, and
android), narrowing the results to those studies related to depression and mobile apps. Search
strategy in S1 Appendix displays the strategy for retrieving relevant manuscripts from PubMed.
The library information specialist did the search in November 2015. During the first level of
screening, two reviwers (AH, SR) independently assessed a random selection of 15% of the
titles and abstracts retrieved from search (350 electronic search results) to determine inter-
rater agreement on inclusion and exclusion criteria. With substantial levels of agreement
(kappa = 0.69) observed [17], the remaining titles and abstracts were screened by only one
reviewer (SR). At the second level of screening, potential relevant full- text articles were
reviewed and a random selection of 30% of articles (a subset of 50 articles) were independently
assessed by two reviewers (AH, SR). Articles were excluded at this stage from further consider-
ation for a number of reasons (i.e., article did not talk about depression, article did not make
mention of any native app, the app mentioned in the article was not addressed to people with
depression, the manuscript was not written in English). With substantial levels of agreement
observed at this second level of screening (kappa = 0.85) [17], the remaining full-text articles
were reviewed by only one reviewer (SR). The 53 manuscripts included at this stage mentioned
a total of 253 native apps for people with depression. Two independent reviewers (SR, AH)
independently evaluated whether a random selection of 50% of these 253 apps (n = 125) meet
the eligibility criteria based on our inclusion/exclusion criteria. With almost perfect agreement
observed at this third level of screening (kappa = 0.92) [17], the remaining apps were reviewed
by only one reviewer (SR). Contact was made with corresponding authors to request access to
any apps described in a manuscript where there was no information provided on public access
for downloading. Discrepancies at any level of screening were resolved by consensus among
reviewers. See Fig 1 for details about the screening process.
The search of the commercial market place. The search was restricted to apps available
through the two most popular mobile phone platforms, The Canadian Apple App Store and
Android Market (Google Play). The search was made in November 2015 using depressionas
the search query. One reviewer (JC) searched the stores to identify all of the available apps, and
two reviewers (AH, JC) independently evaluated each identified unique app for eligibility
based on our inclusion/exclusion criteria. The level of agreement between both independent
reviewers using the Cohens Kappa was 0.89. Discrepancies were resolved through discussion.
See Fig 1 for further details.
Data extraction
The apps retrieved by our searches were categorized by two independent reviewers (AH, JC)
according to the type(s) of support that they offered to the users. The categories, defined a pri-
ori, included: self- tracking tools, education, social support, CBT/BA treatment, state induction,
diagnostic/screening tools, and miscellaneous. One app could be categorized into different
Systematic Review of CBT and BA Apps for Depression
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 3/19
types of self-help apps when the app included more than one type of support. All the apps
included in the review were available in the app stores, regardless of where they were identified
(i.e., scientific literature vs commercial market). The app description displayed in the stores
and any available description provided in the manuscript was the only information used by the
reviewers to base their decisions on which category each app fell into. The level of agreement
between the two reviewers when categorizing the apps, using the Cohens kappa, was 0.92, indi-
cating almost perfect agreement [17]. When reviewers were in disagreement, they discussed it,
and came to an agreement. When an agreement could not be reached, a third reviewer was
called upon (SR). For those apps that were classified as CBT/BA the following information was
extracted: accessibility (i.e., iTunes, Google Play, scientific literature), cost, and indicators of
popularity (i.e., for the apps identified through the Google Play store, the number of times an
app has been downloaded to an android phone; for the apps identified through the Google
Play store or the iTunes store, the number of users that have rated the app on a scale of 1 to 5
as well as the average satisfaction rate provided by users; although both types of information
are only available when there is a large, unspecified amount of users that have rated the app).
Assessment of CBT/BA apps. Since our primary focus of attention was CBT or BA only
those apps that offered this type of treatment were downloaded for full evaluation. When both
a paid and free version of an app was available, the version requiring payment was purchased
and used, while the free version was excluded. This was done to ensure that the most compre-
hensive version of the app was considered. In accordance with Chan et al [16], who have
recently proposed a framework to evaluate mobile mental health apps, we evaluated each app
on three dimensions using the following criteria:
Usefulness: To determine the usefulness of the apps, the validity and accuracy (does the app
actually offer CBT or BA?), and effectiveness (is the app clinically effectivewith demonstrated
improved outcomes- for people with depression?) criteria were used. To evaluate whether the app
actually offers CBT or BA, an experienced academic CBT clinician (SR) evaluated the apps for
their level of fidelity to theoretical CBT and BA principles by exploring what extent the apps
included the core ingredients of these models. The evaluator has extensive experience in training
CBT therapists and devising CBT clinical programmes. The core ingredients for CBT and BA
were derived by consulting with two academic experts and one CBT clinician, as well as reviewing
the literature for CBT and BA models in the treatment of depression [18,19]. The following were
considered as the core ingredients of a CBT approach for depression: 1) education about depres-
sion; 2) explanation of the model, 3) depression rating, 4) monitoring cognitions, 5) monitoring
emotions, 6) monitoring physical sensations, 7) monitoring behaviours, 8) conceptualization, 9)
behavioural techniques, and 10) cognitive techniques. The following were considered as the core
ingredients of the various BA approaches: 1) education about depression, 2) explanation of the
model, 3) depression rating, 4) activity monitoring, 5) giving each activity a rating for pleasure, 6)
giving each activity a rating for mastery, 7) activity scheduling of pleasant behaviours, and 8)
activity scheduling of avoided behaviours. The expert evaluated each app against each core ingre-
dient on a 02 scale where 0 meant that the core ingredient was not integrated at all into the app,
and 2 meant that the core ingredient was completely integrated. Table 1 displays the scoring sys-
tem devised for rating of the apps against each core ingredient. For each app, a percent total score
(sum of item scores/maximum possible score 100), representing the level of adherence of the
app to the theoretical principles of CBT and BA approaches, was then calculated. To evaluate the
effectiveness of the apps, we cross-referenced with apps identified in the scientific literature to see
whether there was any efficacy or effectiveness study on apps included in the review.
Usability: The usability of the app (can the user easilyor with minimal training- use and
understand the app?) was used to evaluate this dimension. Most apps retrieved from our
searches have been developed by small businesses or sole proprietors outside of academic
Systematic Review of CBT and BA Apps for Depression
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 4/19
settings, and little information is available on the app development process or evidence of
formal usability testing. For this reason, a user experience designer (MW), who regulary per-
forms expert reviews on mobile apps and websites, where he applies heuristics and profes-
sional experience to evaluate user interfaces and suggest design improvements, evaluated
the usability of the apps. He evaluated the user interface of each app using a common list of
usability heuristics proposed by Nielsen & Mack [20]. The usability expert rated each app on
a scale of 1 to 5 (1 = poor, 5 = excellent) against each usability heuristic (see Table 2 for the
set of heuristics). A percentage total score (sum of item scores/maximum possible total
score 100) was then calculated, indicating the extent to which the user interface of the app
met the usability heuristics.
Integration and infrastructure: Privacy and safety were the criteria used to evaluate this
dimension. To evaluate privacy, an evaluator (SR) looked into whether the apps provided users
with a privacy policy (within the apps themselves or on a website linked to the app). If a privacy
policy was available the evaluator assessesed the scope and the level of transparency of the pol-
icy as done by Sunyaev et al. [21]. To this end, the evaluator determined whether the policy
addressed the following content categories important to users: type of information collected
(e.g., operational, behavioral, sensitive), rationale for collection (i.e., app operation, personali-
zation, secondary use), sharing of information (i.e., service provision, social interaction, third
party), and users controls (i.e., supervision, notification, correction). To evaluate safety, an
evaluator (SR) explored whether the apps had any mechanisms in place to handle high risk of
suicidality (e.g. providing emergency contact information whenever the app detects a user is at
high risk for committing suicide).
Table 1. System to grade the level of adherence to the theoretical principles of CBT and BA.
Item
Core features 0 1 2
Behavioural activation
Education about depression None Some Clear explanation
Explanation of the model None Some Clear explanation
Depression rating None Some Formally rated (e.g., on a 010 scale)
Activity monitoring None Some Formally self-monitoring (e.g., through a
diary)
Activity monitoring: Pleasure rating None Some Formally rated (e.g., on a 010 scale)
Activity monitoring: Mastery rating None Some Formally rated (e.g., on a 010 scale)
Activity scheduling of pleasant behaviours None Some Formally rated (e.g., on a 010 scale)
Activity scheduling of
avoidancebehaviours
None Some Formally rated (e.g., on a 010 scale)
Cognitive-behavioural treatment
Education about depression None Some Clear explanation
Explanation of the model None Some Clear explanation
Depression rating Some Some Formally rated (e.g., on a 010 scale)
Monitoring cognitions None Some Thoughts and beliefs monitored
Monitoring emotions None Some Specic emotions monitored
Monitoring physical sensations None Some Specic physical sensations monitored
Monitoring behaviours None Some Specic behaviours monitored
Conceptualization None Someelements Adequate problem formulation
Cognitive techniques None Some Systematic use of technique
Behavioural techniques None Some Systematic use of technique
doi:10.1371/journal.pone.0154248.t001
Systematic Review of CBT and BA Apps for Depression
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 5/19
Analysis Plan
Basic summary statistics including counts and percentages were used to describe the character-
istics of the apps. Spearmans correlation coefficient was used to explore whether a relationship
may exist between the adherence of the user interface to Nilsens principles of usability and
adherence to the core principles underlying CBT and BA. Spearmans correlation coefficients
were also used to explore whether adherence to the core principlies underlying CBT and BA
and adherence to Nilsens principles of usability is related with any indicator of popularity and
acceptability (i.e., average rating of satisfaction, number of reviews and number of downloads).
Results
Search
Our search of commercial marketplace identified a total of 310 unique apps. One hundred and
four of these apps identified in the commercial marketplace meet our inclusion/exclusion
Table 2. Heuristics used to assess usability of the apps.
Heuristic Description
Visibility of system status The system should always keep users informed about what is
going on, through appropriate feedback within reasonable time.
Match between system and the real
world
The system should speak the users' language, with words,
phrases and concepts familiar to the user, rather than system-
oriented terms. Follow real-world conventions, making information
appear in a natural and logical order.
User control and freedom Users often choose system functions by mistake and will need a
clearly marked "emergency exit" to leave the unwanted state
without having to go through an extended dialogue. Support undo
and redo.
Consistency and standards Users should not have to wonder whether different words,
situations, or actions mean the same thing. Follow platform
conventions.
Error prevention Even better than good error messages is a careful design which
prevents a problem from occurring in the rst place. Either
eliminate error-prone conditions or check for them and present
users with a conrmation option before they commit to the action.
Recognition rather than recall Minimize the user's memory load by making objects, actions, and
options visible. The user should not have to remember information
from one part of the dialogue to another. Instructions for use of the
system should be visible or easily retrievable whenever
appropriate.
Flexibility and efciency of use Acceleratorsunseen by the novice usermay often speed up
the interaction for the expert user such that the system can cater
to both inexperienced and experienced users. Allow users to tailor
frequent actions.
Aesthetic and minimalist design Dialogues should not contain information which is irrelevant or
rarely needed. Every extra unit of information in a dialogue
competes with the relevant units of information and diminishes
their relative visibility.
Help users recognize, diagnose, and
recover from errors
Error messages should be expressed in plain language (no
codes), precisely indicate the problem, and constructively suggest
a solution.
Help and documentation Even though it is better if the system can be used without
documentation, it may be necessary to provide help and
documentation. Any such information should be easy to search,
focused on the user's task, list concrete steps to be carried out,
and not be too large.
doi:10.1371/journal.pone.0154248.t002
Systematic Review of CBT and BA Apps for Depression
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 6/19
criteria. The literature search yielded 2,789 abstracts, and 160 full text manuscripts were
reviewed at the full-text level. Fifty-three out of 160 were relevant for our review because all
them mention at least one native app addressed to people for depression. Many of these manu-
scripts identified as relevant for our review were reports or reviews reporting on multiple apps.
For example, Shen et al. [14], has recently conducted a systematic review to identify and char-
acterize all the apps available in the app stores to support people with depression, their families
and health care professionals, based on the store description. The 53 manuscripts [9,14,15,22,
23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,
53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71] identified as relevant to the review
made mention of a total of 48 unique apps that met our inclusion/exclusion criteria. Thirty-
five of these 48 apps were also identified through our search of the commercial marketplace.
See Fig 1 for a flowchart of the screening process of the apps.
App characteristics
Out of the total 117 apps, 36 apps (30.77%) were available on iOS only, 74 (63.25%) were avail-
able on Android only, and 7 (5.98%) were available across both platforms. The most typical
type of self-help support delivered through these 117 apps was education (n = 32, 27.35%) and
diagnostic/screening support (n = 30, 25.64%), followed by state induction (n = 18, 15.38%).
The least typical types of self-help support delivered through these 117 apps were tracking
(n = 10, 8.55%) and social support (n = 3, 2.56%). Twelve of these 117 apps (10.26%) were clas-
sified by the reviewers as delivering CBT or BA; these CBT/BA apps were identified in the
description by their developers as CBT or BA apps or they seemed to offer CBT or BA based
on their general description (Table 3).
CBT/BA apps characteristics
Five of the 12 CBT/BA apps (41.67%) were available on iOS only and 5 (41.67%) on Android
only. The cost of these CBT/BA apps ranged from $0.00 to $8.99. The Depression CBT Self-
Help Guide and The Mood ToolsDepression Aid were those Android apps with the highest
number of downloads (i.e., between 100,000 and 500,000 downloads, and between 50,000 and
100,000, respectively) and received high user satisfaction ratings (average satisfaction rates
were 4.2 and 4.3, respectively). The iPhone app that received the highest user satisfaction rating
was The Depression Cure: The Free 12 Week Course app (average satisfaction rating = 4.5).
However, this app was not the one that received the highest number of reviews. The iPhone
apps that received the highest number of reviews were the Anti-depression and MoodTools
Depression Aid apps, both of them also available for download in the Google Play store. For
further information about the characteristics of the CBT/BA apps see Table 4.
Regarding the validy and accuracy of the CBT/BA apps, the median level of adherence with
the CBT principles was 15% (range = 075%) and the median level of adherence with the BA
principles was 18.75% (range = 6.2525%). The best apps from a theoretical perspective were
Depression CBT Self-Help Guide and eCBT Mood meeting 75% and 55% of the qualifying cri-
teria for CBT, respectively. The rest of the apps presented less than 50% of adherence for both
the CBT and BA principles (see Table 5). The core ingredients of CBT most commonly
included in these CBT/BA apps were: education about depression and depression ratings. The
core ingredients included least often were: monitoring physical sensations, monitoring behav-
iors, and conceptualization. The core ingredients of BA most commonly included were: educa-
tion about depression and depression ratings and the rest of the core ingredients were never
completely integrated into the apps. Regarding the effectiveness of the apps, there were no
Systematic Review of CBT and BA Apps for Depression
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 7/19
studies reported in the scientific literature that determined the benefits of any of these CBT/BA
apps.
The usability heuristic evaluation found that the median level of adherence with the heuris-
tics was 83% (range = 4298%). The apps associated with highest usability ratings were Mood
ToolsDepression Aids, Activity Diary, and Depression on CureThe Free 12 Week Course
Table 3. All apps for depression included in the review.
Type of Self-Help
App
Screening Number of
Apps
a
Total Number of
apps (%)
b
Name of the Apps
Tracking Commercial
Marketplace
6 10 (8.55) Depression Test, Depression Tracker and Diary, Emotion, Life Robot- Fight Depression,
Mood tracker- depression, Start
Literature 1 iDepression Tracker
Both 3 Depression Inventory, Depression Journal, Depression Test & Tracker
CBT/BA Commercial
Marketplace
4 12 (10.26) MoodTools- Depression Aid, Overcome the Depression pro, Anti-Depression, Activity Diary
Both 8 Depression, Depression CBT Self- Help Guide, Depression Cure- The free 12 week course,
iCounselor: Depression, Mood Master Anti- Depression App, Mood Sentry, Positive Activity
Jackpot, eCBT Mood
State Induction Commercial
marketplace
10 18 (15.38) Beat Depression Hypnosis Audio, Depression Cure Hypnosis, Depression Mood Booster,
Fight Depression, From Depression to Hope, MoodSpace, Vital Tones Depression, Yoga for
Depression, Yoga Helps Relieve Depression, Life Robot- Fight Depression
Literature 2 Depression Relief and MoodHappyApp,Mood Elevator & Support
Both 6 Beat Depression Hypnosis Syste, Depression Help Brainwave, Depression Inventory, Heal
Depression Hypnosis, The Mindful Way Through Depression, Black Rainbow: How to Beat
Depression
Diagnostic/
screening
Commercial
Marketplace
15 30 (25.64) CESD Depression Test, Depression Diagnosis Doctor, Depression Eval Questionnaire,
Depression Screening Test, Depression Test, Depression Test, Depression Test,
Depression Test, Depression Test, Depression Test, Depression Test and Treatment,
Depression Tracker & Diary, Depression Test Pro, Depression Test, Emotion
Literature 4 Am I Depressed, Do I have Depression, Happy App, Zung
Both 11 Are You at Risk for Depression?, Depression Calculator, Depression Screening, Depression
Test, Depression Test, Learn About Depression, Major Depression Checker, Sad Scale Lite,
STAT Depression Screening PHQ 9, The Depression Predictor, Depression Test & Tracker
Education Commercial
Marketplace
22 32 (27.35) Conquering Depression, Dealing with Depression, Dealing with Depression, Depression,
Depression and How to Stop it, Depression & Psychology, Depression Denition,
Depression Healing, Depression Information, Depression Management, Depression
Symptoms, Depression Symptoms and Signs, Depression: An Overview, Depression:
Natural Remedies, Fitness Against Depression, Help with Depression, How to get Over
Depression, Physical Symptoms Depression, Reduce Depression, The Key to Happiness,
Black Rainbow: How to Beat Depression, Emotion
Literature 3 Beat Depression, Depression Treatment, Overcoming Depression
Both 7 Depression 101, Depression Advice, NIH Depression Information, Ten Tips to Ease
Depression, The Depression Predictor, Are You at Risk for Depression?, Depression
Preview
Social Support Commercial
Marketplace
2 3 (2.56) Dealing with Depression, You Are Important
Both 1 Depression Test
Unclear/
miscellaneous
Commercial
Marketplace
22 28 (23.93) A Guiding Light, Acupuncture Against Depression, Afternoon in Depression, Best
Depression Quotes, Depression, dePRESSION, Depression Quotes Wallpaper, Depression
Quotes Wallpaper, Depression- Acupuncture, Depressive and Sad Wallpaper, Endless
Depression, Get Rid of Depression with Chinese Massage Points, Guide to Depression Self-
Help, How to Beat Depression, Sad Quotes Wallpaper, Sadness and Depression Quotes,
Secret of Happiness, Self-Help for Depression, MoodSpace, Life Robot- Fight Depression,
Depression Management, You are Important
Literature 3 Depressed, DepressPill Game for Happy, Joker
Both 3 Anti-Depression Grocery List, Depression Fighter- A Practical Christian Guide, Surviving
Depression
a
The total sum of number of apps for each type of self-help app is not equal to the total number of apps identied through our searches (n = 117), since
some apps have been categorized into multiple categories.
b
Percentage of the total 117 apps
doi:10.1371/journal.pone.0154248.t003
Systematic Review of CBT and BA Apps for Depression
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 8/19
scoring 98%, 98%, and 92% respectively. The most frequent heuristic violations of these CBT/
BA apps were: visibility of the system status, and consistency and standards. See Table 6.
Only the eCBT app and the Depression CBT Self-Help Guide app offer a privacy policy.
The eCBTapp has a brief privacy policy that states that the information collected in the app is
only accessed by the application on the device and they do not collect any information about
the user or the use of the app. The Depression CBT Self-Help Guide apps privacy policy
applies to this app in particular, but also other products of its developer (other apps and its
homepage). This policy is available on the developers homepage but is also available to users
after they have downloaded the app. Its privacy policy indicates what information is collected
and for what purpose, whether this information is shared with others but it does not address
users control. Five out of the 12 apps (41.66%) provide important safety information during
crisis.See Table 7 for details about what information is provided and how.
No relationship was found between the level of adherence of the app to the theoretical CBT
or BA model and the level of adherence with the heuristics usability (r
s
= -0.45, p = 0.13 and r
s
= 0.30, p = 0.33, respectively). Also, no relationship was found between level of adherence of
the app to the theoretical models and the indicators of popularity (range = r
s
= -0.02, p = 0.96
Table 4. Currently-available CBT or BA apps for depression.
Name Author Commercial
Market
Scientifc
Literature
Cost Popularity Adherence
with the core
ingredients
Adherencewith
the heuristics
iTunes Google
Play
ASR
a
#of
reviews
#of
downloads
CBT BA
Depression Cure:
The free 12 week
course
Archies
Empire
$8.99 4.5 29 n/a
b
10% 18.75% 92%
iCounselor:
Depression
iCounselor $1.19 2.5 9 n/a
b
5% 6.25% 60%
Mood Master
Anti depression
App
Mood Master $4.59 n/a
b
n/a
b
n/a
b
15% 18.75% 82%
eCBT Mood MindApps
LLC
$1.19 2.5 12 n/a
b
55% 25% 70%
Activity Diary Happtic Pty.
Ltd
✗✗$3.49 n/a
b
n/a
b
n/a
b
0% 18.75% 98%
Anti-depression Dion LLC ✓✓ $0 3.7 250 10,000
50,000
25% 25% 88%
MoodTools
Depression Aid
MoodTools ✓✓ $0 4.3 1,466 50,000
100,000
10% 12.5% 98%
Mood Sentry Mood Apps
LLC
✓✓$1.97 5.0 3 50100 25% 6.25% 42%
Depression AppCounselor ✓✓$0.99 4.0 5 5001,000 15% 6.25% 64%
Depression CBT
Self-Help Guide
Excel at Life ✓✓$0 4.2 1,154 100,000
500,000
75% 25% 62%
Overcome the
Depression Pro
Zanapps $0 4.2 5 100500 20% 18.75% 84%
Positive Activity
Jackpot
T2 ✓✓$0 3.4 74 10,000
50,000
0% 12.5% 84%
a
ASR- Average Satisfaction Rating
b
Some apps do not have enough user reviews to have an average rating, stated by n/a.
doi:10.1371/journal.pone.0154248.t004
Systematic Review of CBT and BA Apps for Depression
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 9/19
Table 5. Evaluation of the usefulness dimension.
Usefulness
Dimensions
Name
Depression
Cure- the
Free 12
Week
Course
iCounselor:
Depression
MoodMaster-
Anti
Depression
App
eCBT
Mood
Activity
Diary
Anti-
Depression
MoodTools-
Depression
Aid
Mood
Sentry
Depression Depression
CBT Self-
Help Guide
Overcome
the
Depression
Pro
Positive
Activity
Jackpot
Total
Proven Efcacy ✗✗ ✗✗✗
Core Ingredients
of CBT
Education about
depression
10 220201021011
Explanation of the
model
00 02000002004
Depression rating 0 1 1 2 0 1 0 0 0 2 2 0 9
Monitoring
cognitions
00 02000112107
Monitoring
emotions
00 01000102004
Monitoring
physical
sensations
00 00010000001
Monitoring
behaviors
00 00000001001
Conceptualization 0 0 0 1 0 0 0 0 0 1 0 0 2
Cognitive
techniques
00 01001112006
Behavioral
techniques
10 00011111006
Total score out of
20 (%)
2 (10) 1 (5) 3 (15) 11
(55)
0 (0) 5 (25) 2 (10) 5 (25) 3 (15) 15 (75) 4 (20) 0 (0)
Core Ingredients
of BA
Education about
depression
10 220121021012
Explanation of the
model
00 00000000000
Depression rating 0 1 1 2 0 1 0 0 0 2 2 0 9
Activity monitoring 0 0 0 0 1 1 0 0 0 0 0 0 2
Activity monitoring:
Pleasure rating
00 00110000002
Activity monitoring:
Masteryrating
00 00100000001
Activity scheduling
of
pleasantbehaviors
10 00000010013
Activity scheduling
of avoidance
behaviors
10 00000000012
Total score out of
16 (%)
3 (18.75) 1 (6.25) 3 (18.75) 4 (25) 3
(18.75)
4 (25) 2 (12.5) 1
(6.25)
1 (6.25) 4 (25) 3 (18.75) 2 (12.5)
doi:10.1371/journal.pone.0154248.t005
Systematic Review of CBT and BA Apps for Depression
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 10 / 19
Table 6. Evaluation of the usability dimension.
Name Visibilityof
system
status
Match
between
system
and the
real
world
User
controland
freedom
Consistency
and
standards
Error
prevention
Recognition
rather than
recall
Flexibilityand
efciency of
use
Aestheticand
minimalist
design
Help users
recognize,
diagnose,
and recover
from errors
Help and
document-
ation
Total
score
out of
50(%)
Depression
CureThe
Free 12
Week Course
455 4 5 4 5 4 5 546
(92)
iCounselor:
Depression
153 2 5 2 2 3 5 230
(60)
MoodMaster
Anti-
Depression
App
354 3 5 4 4 4 5 441
(82)
eCBT Mood 2 5 4 2 5 2 3 5 5 2 35
(70)
Activity Diary 4 5 5 5 5 5 5 5 5 5 49
(98)
Anti-
depression
454 5 5 5 3 4 5 444
(88)
Mood Tools
Depression
Aids
554 5 5 5 5 5 5 549
(98)
Mood Sentry 2 4 3 2 1 2 2 3 1 1 21
(42)
Depression 2 5 3 2 5 2 2 3 5 3 32
(64)
Depression
CBT Self-
Help Guide
244 2 4 2 3 2 4 431
(62)
Overcome
the
Depression
Pro
545 3 5 5 4 2 4 542
(84)
Positive
Activity
Jackpot
554 4 5 5 3 2 5 442
(84)
Total 39 57 48 39 55 43 41 42 54 44
doi:10.1371/journal.pone.0154248.t006
Systematic Review of CBT and BA Apps for Depression
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 11 / 19
Table 7. Evaluation of integration and infrastructure dimension.
Name Do they
have privacy
policy?
Scope of privacy
policy
Transparency of privacy policy Do they
deal with
safety?
How?
Type of
information
collected
Rationale for
collection
Sharing of
information
User
control
Depression Cure-
the Free 12 Week
Course
✗ ✗
iCounselor:
Depression
✗ ✗
MoodMaster- Anti
Depression App
✗ ✗
eCBT Mood Single app n/a
a
n/a
a
n/a
a
If the user scores high on depression, they are
encouraged to contact health care provider or crisis
center (number is provided).
Activity Diary ✗ ✗
Anti-Depression ✗ ✗
MoodTools-
Depression Aid
Includes a safety plan feature, where user can input
infomation about crisis warning signs, coping
strategies, reasons to live, and add contacts to call.
There is also a ?icon which gives the user the
option to work with a therapist, allow user to visualize
a safety plan video, and give them a direct link to call a
help line.
There is a guide which goes through different stages
from coping, to recovery, suicide prevention.
Static crisis tab with 4 different options; call 911, call
helpline, and a map feature to either nd urgent care
or the nearest emergency department.
Mood Sentry ✗ ✗
Depression Once user provides a high rating of depression, a
safety screen with information appears.
Static tab for the same safety screen appears within
the learning module.
Depression CBT
Self-Help Guide
This app plus other
apps created by the
developer, and the
homepage
✓✓
Overcome the
Depression Pro
The user is encouraged to seek professional help if
they score high on depression.
Positive Activity
Jackpot
In the license that user rst sees upon enterting app,
they make a brief statement about if user is in an
emergency or life threatening situation to seek medical
assistance or dial emergency number.
In the settings there is a statement that says if at any
point you feel suicidal please call crisis care hotline
(number is provided).
a
All the information collected is stored on the device and can only be accessed by the user. Developer does not collect/store any information about the user or the use of the app.
doi:10.1371/journal.pone.0154248.t007
Systematic Review of CBT and BA Apps for Depression
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 12 / 19
Fig 1. Flowchart of the screening process.
doi:10.1371/journal.pone.0154248.g001
Systematic Review of CBT and BA Apps for Depression
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 13 / 19
and r
s
= 0.57, p = 0.18), or between level of adherence of the app with the heuristic usability
with the indicators of popularity (range = r
s
= 0.15, p = 0.68 and r
s
= 0.59, p = 0.07).
Discussion
While there are a large number of phone apps designed to assist those with depression available
through the commercial market, few of these utilize a CBT or BA approach despite these being
the gold standard of first line psychological treatments [72]. The few apps that provide CBT or
BA seem to be popular based on the number of downloads, with 4 out of 7 of the Android
available apps achieving more than ten thousand downloads.
Chan et al. [16] have recently proposed a framework that can be used for patients and
health care providers to evaluate existing mental health mobile apps and help them make
informed choices about their use. Chan et al. [16] suggest evaluating apps on three broad
dimensions: usefulness, usability, and integration/infrastructure. After evaluating the useful-
ness dimension of the CBT/BA apps taking into account the main usefulness criteria of
effectiveness, we can see that there is no available information on effectiveness. The few
available apps that offer CBT or BA have either not been tested or the results derived from
these tests have not been reported in the scientific literature. This means that we do not have
any direct evidence demonstrating the efficacy of these CBT/BA apps and consequently we
do not have direct scientific proof to support their use. All the apps identified through
searching the scientific literature were simply cited in reviews [14]; they were not evaluated
in primary research studies. Although no data on the efficacy of these CBT/BA apps have
been published, we need to acknowledge that evidence may exist outside scientific journals.
Knowledge can be disseminated through grey literature. The lack of direct scientific evidence
for these CBT/BA apps, however, becomes especially alarming after evaluating the validity
and accuracy of the content of these apps from an experts point of view. Of those apps which
do use CBT or BA, some apps may provide benefits by partially applying CBT or BA princi-
ples, but the majority do not come close to including the core ingredients of a CBT or BA
program. The lack of fidelity to proven CBT or BA principles could hamper the efficacy of
these programs.
When evaluating the usability dimension, we have seen that the usability of the available
CBT/BA apps is highly variable and likely serves as a barrier to adoption and regular usage for
those apps that violate a large number of heuristics. For instance, the Depression CBT Self-
Help Guide app has the highest fidely to CBT models, but the low usability score could compli-
cate its use. There is a danger that users of these available CBT/BA apps may interpret ineffec-
tiveness as a treatment failure, when in fact, ineffectiveness may be the result of usability
problems or the inappropriate application of the CBT or BA model.
On the one hand, there doesnt appear to be a correlation between CBT/BA model adher-
ence and usability, which means that a good application of the clinical theoretical CBT or BA
knowledge when designing the app does not imply a good use of principles of usability, and/or
vice versa. On the other hand, the degree to which the apps contain these core ingredients of
the CBT and BA models does not appear to be correlated with the extent to which users like
the app, the number of downloads, or the number of reviews for the app. Equally, the level of
usability of the CBT/BA apps does not appear to be correlated with the extent to which users
like the app, the number of downloads or the number of reviews for the app. This finding is
not surprising; previous reviews have found no relationship between the quality of the apps
and consumers reviews or ratings [73,74]. Therefore, users should be careful when using the
information available on the app download page to judge the app, since this information can
be misleading.
Systematic Review of CBT and BA Apps for Depression
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 14 / 19
When evaluating the integration and infrastructure dimension, we have seen that safety
information is not always available in apps, and very rarely are users provided with a privacy
policy. This lack of availability of privacy information seems to be an issue for mental health
apps in general [21]. Research has shown that privacy is a concern for many health care profes-
sionals and patients [75] and this concern is a reason for them to decline the use information
technology [75,76,77] as part of their care.
We have identified through our systematic review four apps in English that offer CBT or
BA treatment for depression and have been studied by researchers and published in scientific
papers, the Behavioural Activation Scheduling [50], the Get Happy Program [40], CBT
Mobilwork [65]andMobilyze[45]. However, these four apps have not been included in our
full analysis because they are not currently available for download by the public, at least from
within Canada. The lack of empirically tested apps identified during this review is consistent
with observations in other health fields [36] and raises concerns about relying on these tools
to support treatment for depression. We therefore launch a call for scientists and/or app
developers interested in the opportunities that mobile communication technology offers in
terms of improving access to mental health care to test the existing best apps and determine
from the outset how to best implement and sustain the apps over time given that technology
is evolving rapidly. It is also important when designing new CBT/BA apps to try to integrate
the core ingredients of these theoretical models, and to address the heuristics in order to opti-
mize clinical benefits and make the app more usable.Finally,itisimportantthatscientists
and developers are more transparent about legal and regulatory aspects of the apps related to
privacy issues (e.g., [78]). Failure to effectively plan for sustainable dissemination of apps as
well as the lack of consideration of legal aspects may present significant barriers for using
apps.
This review is not without limitations. First, this review was limited to English download-
able apps in Canada and only looked at the two most popular platforms when exploring the
commercial market. Different apps may be available on less prevalent platforms or in other lan-
guages and/or countries, and in fact we excluded apps developed and tested in the academic
setting for these reasons[9,40]. Second, the evaluation of the CBT and BA apps was based on
the opinion of one expert. Although expert opinion plays an important role when no research
evidence exists, the use of an expert panel instead of only one expert could have increased the
credibility of the conclusions. Finally, although it was not the primary goal of this review, the
lack of common constructs, outcome measures, definitions and/or standards for tracking, state
induction, diagnostic/screening, and education apps make cross-case comparison of these dif-
ferent types of self-help apps impossible.
In summary, given the prevalence of depression [1] and the known effectiveness of CBT
and BA in addressing this mental health condition [6,7], a mobile app based on clinical best
practice, that meets the most basic usability standards, that is evaluated scientifically, has a pri-
vacy policy, and deals with safety matters has the potential to remove barriers to care and alle-
viate suffering for a large number of people with depression at a modest cost. Therefore, efforts
towards achieving this are necessary.
Supporting Information
S1 PRISMA Checklist. PRISMA 2009 Checklist.
(DOCX)
S1 Appendix. Search strategy used for Pubmed.
(DOCX)
Systematic Review of CBT and BA Apps for Depression
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 15 / 19
Author Contributions
Conceived and designed the experiments: AH SR (second author) PJM. Performed the experi-
ments: AH SR (seventh author) MW LW JC SR (second author). Analyzed the data: AH SR
(seventh author) MW LW JC SR (second author). Wrote the paper: AH.
References
1. Kessler RC, Chiu WT, Demler O, Waters EE. Prevalence, severity, and comorbidity of 12-month DSM-
IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005; 62: 61727.
PMID: 15939839
2. Kessler RC, Avenevoli S, Merikangas KR. Mood disorders in children and adolescents: an epidemio-
logic perspective. Biol Psychiatry. 2001; 49: 10021014. PMID: 11430842
3. Mathers C, Boerma T, Ma Fa D. The Global Burden of Disease: 2004 Update. 2008. Available:http://
www.who.int/healthinfo/global_burden_disease/GBD_report_2004update_full.pdf
4. Kessler RC. The costs of depression. Psychiatr Clin North Am. 2012; 35: 114. doi: 10.1016/j.psc.
2011.11.005 PMID: 22370487
5. National Collaborating Centre for Mental Health. Depression: The treatment and management of
depression in adults. 2010. Available: https://www.nice.org.uk/guidance/cg90/evidence/cg90-
depression-in-adults-full-guidance2
6. Cuijpers P, Van Straten A, Warmerdam L. Behavioral activation treatments of depression: a meta-anal-
ysis. Clin Psychol Rev. 2007; 27: 31826. PMID: 17184887
7. Butler AC, Chapman JE, Forman EM, Beck AT. The empirical status of cognitive-behavioral therapy: A
review of meta-analyses. Clin Psychol Rev. 2006; 26: 1731. PMID: 16199119
8. Van't Hof E, Cuijpers P, Stein DJ. Self-help and Internet-guided interventions in depression and anxiety
disorders: a systematic review of meta-analyses. CNS Spectr. 2009; 14: 3440.
9. Ly KH, Truschel A, Jarl L, Magnusson S, Windahl T, Johansson R., et al. Behavioural activation versus
mindfulness-based guided self-help treatment administered through a smartphone application: A ran-
domised controlled trial. BMJ Open. 2014; 4: e003440 doi: 10.1136/bmjopen-2013-003440 PMID:
24413342
10. Moss K, Scogin F, Di Napoli E, Presnell A. A self-help behavioral activation treatment for geriatric
depressive symptoms. Aging Ment Health. 2012; 16: 62535. doi: 10.1080/13607863.2011.651435
PMID: 22304676
11. Irvine M. Report: More youth use smartphones as route to Web. 2013 March 13 [cited 6 September,
2014] The Big Story. [Internet]. Chicago: AP News-. [about 4 screens]. Available: http://bigstory.ap.org/
article/report-more-youth-use-smartphones-route-web.
12. International Data Corporation. Smartphones Expected to Grow 32.7% in 2013 Fueled By Declining
Prices and Strong Emerging Market Demand, According to IDC. 2013 June 4 [cited 6 September,
2014] IDC. [Internet]. Massachusetts: Business Wire-. [about 3 screens]. Available: http://www.idc.
com/getdoc.jsp?containerId=prUS24143513.
13. Michael S. Application of smartphone technology in the management andtreatment of mental illnesses.
MPH. Thesis, University of Pittsburgh. 2011. Available: http://d-scholarship.pitt.edu/6783/
14. Shen N, Levitan M, Johnson A, Bender J, Hamilton-Page M,Jadad A, et al. Finding a depression App:
A review and content analysis of the depression app marketplace. J Med Internet Res. 2015; 3: e16
15. Torous J, Powell AC. Current research trends in the use of smartphone applications for mood disorders.
Internet Interv. 2015; 2: 169173.
16. Chan S, Torous J, Hinton L, Yellowlees P. Towards a framework for evaluating mobile mental health
apps. Telemed J E Health 2015; 12: 103841.
17. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;
33: 159174 PMID: 843571
18. Kanter JW, Manos RC, Bowe WM, Baruch DE, Busch AM., Rusch LC. What is behavioral activation? A
review of the empirical literature. Clin Psychol Rev. 2010; 30: 60820. PMID: 20677369
19. Beck AT. The current state of cognitive therapy: a 40-year retrospective. Arch Gen Psychiatry. 2005; 62
(9):9539. PMID: 16143727
20. Nielsen J, Mack RL. Usability inspection methods: Heuristic evaluation. New York: John Wiley &
Sons; 1994.
Systematic Review of CBT and BA Apps for Depression
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 16 / 19
21. Sunyaev A, Dehling T, Taylor PL, Mandl KD. Availability and quality of mobile mental health app privacy
policies. J Am Med Inform Assoc. 2015; 22(e1):e2833. doi: 10.1136/amiajnl-2013-002605 PMID:
25147247
22. Torous J, Staples P, Shanahan M, Lin C, Peck P, Keshavan M, et al. Utilizing a Personal Smartphone
Custom App to Assess the Patient Health Questionnaire-9 (PHQ-9) Depressive Symptoms in Patients
With Major Depressive Disorder. J Med Internet Res. 2015; 2(1): p. e8. doi: 10.2196/mental.3889
23. Gordon M, Henderson R, Homes JH, Wolters MK, Bennett IM, SPIRIT (Stress in Pregnancy: Improving
Results with Interative Technology) Group. Participatory design of ehealth solutions for women from
vulnerable populations with perinatal depression. J Am Med Inform Assoc. pii. ocv109. 2015. doi: 10.
1093/jamia/ocv109
24. Juengst SB, Graham KM, Pulantara IW, McCue M, Whyte EM, Dicianno BE, et al. Pilot feasibility of an
mHealth system for conducting ecological momentary assessment of mood-related symptoms follow-
ing traumatic brain injury. Brain Inj. 2015; 29(11): p. 135161. doi: 10.3109/02699052.2015.1045031
25. Roepke AM, Jafee SR, Riffle OM, McGonigal J, Broome R, Mawell B. Randomized Controlled Trial of
SuperBetter, a Smartphone-Based/Internet-Based Self-Help Tool to Reduce Depressive Symptoms.
Games Health J. 2015; 4(3): p. 23546. doi: 10.1089/g4h.2014.0046 PMID: 26182069
26. Watanabe N, Horikoshi M, Yamada M, Shimodera S, Akechi T, Miki K, et al.Adding smartphone-based
cognitive-behavior therapy to pharmacotherapy for major depression (FLATT project): study protocol
for a randomized controlled trial. Trials. 2015; 16:293. p. doi: 10.1186/s13063-015-0805-z PMID:
26149441
27. Aboujaoude E, Salame W, Naim L. Telemental health: A status update. World Psychiatry. 2015; 14(2):
p. 22330. doi: 10.1002/wps.20218 PMID: 26043340
28. Ly KH, Topooco N, Cederlund H, Wallin A, Bergström J, Molander O,et al. Smartphone-Supported ver-
sus Full Behavioural Activation for Depression: A Randomised Controlled Trial. PLoS One. 2015; 10
(5): p. e0126559. doi: 10.1371/journal.pone.0126559 PMID: 26010890
29. Bitsch JÁ, Ramos R, Ix T, Ferrer-Cheng PG, Wehrle K.Psychologist in a pocket: towards depression
screening on mobile phones. Stud Health Technol Inform. 2015; 211: p. 1539. PMID: 25980862
30. Jiménez-Serrano S, Tortajada S, García-Gómez JM.A Mobile Health Application to Predict Postpartum
Depression Based on Machine Learning. Telemed J E Health. 21(7): p. 56774. doi: 10.1089/tmj.
2014.0113 PMID: 25734829
31. Bush NE, Ouellette G, Kinn J.Utility of the T2 Mood Tracker mobile application among army warrior
transition unit service members. Mil Med. 2014; 179(12): p. 14537. doi: 10.7205/MILMED-D-14-
00271 PMID: 25469967
32. BinDhim NF, Shaman AM, Trevena L, Basyouni MH, Pont LG, Alhawassi TM.Depression screening via
a smartphone app: cross-country user characteristics and feasibility. J Am Med Inform Assoc. 2015; 22
(1): p. 2934. doi: 10.1136/amiajnl-2014-002840 PMID: 25326599
33. Clarke J, Proudfoot J, Birch MR, Whitton AE, Parker G, Manicavasagar V, Harrison V, Christensen H,
Hadzi-Pavlovic D. Effects of mental health self-efficacy on outcomes of a mobile phone and web inter-
vention for mild-to-moderate depression, anxiety and stress: secondary analysis of a randomised con-
trolled trial. BMC Psychiatry. 2014; 14:272.(doi): p. doi: 10.1186/s12888-014-0272-1 PMID: 25252853
34. Lee MD, Kang X, Hanrahan N.Addressing Cultural Contexts in the Management of Stress via Narrative
and Mobile Technology. Stud Health Technol Inform. 2014; 199: p. 1737. PMID: 24875715
35. Elias BL, Fogger SA, McGuinness TM, D'Alessandro KR.Mobile apps for psychiatric nurses. J Psycho-
soc Nurs Ment Health Serv. 2014; 52(4): p. 427. doi: 10.3928/02793695-20131126-07 PMID:
24305909
36. Donker T, Petrie K, Proudfoot J, Clarke J, Birch MR, Christensen H. Smartphones for smarter delivery
of mental health programs: a systematic review. J Med Internet Res. 2013; 15(11): p. e247. doi: 10.
2196/jmir.2791 PMID: 24240579
37. Proudfoot J, Clarke J, Birch MR, Whitton AE, Parker G, Manicavasagar V, et al.Impact of a mobile
phone and web program on symptom and functional outcomes for people with mild-to-moderate
depression, anxiety and stress: a randomised controlled trial. BMC Psychiatry. 2013; 13:312. p. doi:
10.1186/1471-244X-13-312 PMID: 24237617
38. Schaffer A, Kreindler D, Reis C, Levitt AJ. Use of mental health telemetry to enhance identification and
predictive value of early changes during augmentation treatment of major depression. J Clin Psycho-
pharmacol. 2013; 33(6): p. 77581. doi: 10.1097/JCP.0b013e31829e8359 PMID: 24100787
39. Martínez-Pérez B, de la Torre-Díez I, López-Coronado M. Mobile health applications for the most prev-
alent conditions by the World Health Organization: review and analysis. J Med Internet Res. 2013; 15
(6): p. e120. doi: 10.2196/jmir.2600 PMID: 23770578
Systematic Review of CBT and BA Apps for Depression
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 17 / 19
40. Watts S, Mackenzie A, Thomas C, Griskaitis A, Mewton L, Williams A, Andrews G.CBT for depression:
a pilot RCT comparing mobile phone vs. computer. BMC Psychiatry. 2013; 13:49.(doi): p. doi: 10.1186/
1471-244X-13-49 PMID: 23391304
41. Kauer SD, Reid SC, Crooke AH, Khor A, Hearps SJ, Jorm AF,et al. Self-monitoring using mobile
phones in the early stages of adolescent depression: randomized controlled trial. J Med InternetRes.
2012; 14(3): p. e67. doi: 10.2196/jmir.1858 PMID: 22732135
42. Ly KH, Carlbring P, Andersson G. Behavioral activation-based guided self-help treatment administered
through a smartphone application: study protocol for a randomized controlled trial. Trials. 2012; 13:62.
(doi): p. doi: 10.1186/1745-6215-13-62
43. Reid SC, Kauer SD, Hearps SJC, Crooke AHD, Khor AS, Sanci LA, et al. A mobile phone application
for the assessment and management of youth mental health problems in primary care: a randomised
controlled trial. BMC Fam Pract. 2013; 12:131.(doi): p. doi: 10.1186/1471-2296-12-131
44. Harrison V, Proudfoot J, Wee PP, Parker G, Pavlovic DH, Manicavasagar V. Mobile mental health:
review of the emerging field and proof of concept study. J Ment Health. 2011; 20(6): p. 50924. doi: 10.
3109/09638237.2011.608746
45. Burns MN, Begale M, Duffecy J, Gergle D, Karr CJ, Giangrande E, Mohr DC. Harnessing context sens-
ing to develop a mobile intervention for depression. J Med Internet Res. 2011; 13(3): p. e55. doi: 10.
2196/jmir.1838 PMID: 21840837
46. Giridher T, Wasilewska A, Wong, JL, Rekhi KS. Global mobile applications for monitoring health. in
Computer Science and Information Technology (IMCSIT), Proceedings of the 2010 International Multi-
conference on.
47. Parish C.Mental Health Library apps. Nursing standard (Royal College of Nursing (Great Britain):
1987). 29(31): p. 3333.
48. Karasouli E, Adams A. Assessing the evidence for e-Resources for mental health self-management: A
Systematic literature review. JMIR mental health. 1(1): p. e3e3. doi: 10.2196/mental.3708 PMID:
26543903
49. Wan J, Hu B, Moore P, Ashford R. Intelligent mobile computing to assist in the treatment of depression.
2008 3rd International Conference on Pervasive Computing and Applications, Vols 1 and 2. 2008. 652
657.
50. Both F, Cuijpers P, Hoogendoorn M, Klein M. Towards fully automated psychotherapy for adults-BAS-
Behavioral Activation Scheduling via web and mobile phone. Healthinf 2010: Proceedings of the Third
International Conference on Health Informatics, ed. A. Fred, J. Filipe, and H. Gamboa. 375380.
51. Proudfoot J.The future is in our hands: The role of mobile phones in the prevention and management of
mental disorders. Australian N Z J Psychiatry. 2013; 47(2): p. 111113.
52. Reid SC, Kauer SD, Hearps SJC, Crooke AHD, Khor AS, Sanci LA, Patton GC. A mobile phone appli-
cation for the assessment and management of youth mental health problems in primary care: health
service outcomes from a randomised controlled trial of mobiletype. BMC family practice. 2013; 19(14):
84. doi: 10.1186/1471-2296-14-84
53. Price M, Yuen EK, Goetter EM, Herbert JD, Forman EM, Acierno R, Ruggiero KJ. mHealth: A Mecha-
nism to Deliver More Accessible, More Effective Mental Health Care. Clinical Psychol & Psychother.
2014; 21(5): p. 427436.
54. Reyes-Portillo JA, Mufson L, Greenhill LL, Gould MS, Fisher PW, Tarlow N,Rynn MA. Web-based inter-
ventions for youth internalizing problems: A Systematic Review. Journal of the American Academy of
Child and Adolescent Psychiatry. 2014; 53(12): p. 12541270. doi: 10.1016/j.jaac.2014.09.005 PMID:
25457924
55. Gravenhorst F, Muaremi A, Bardram J, Grunerbl A, Mayora O, Wurzer G, Frost M, et al. Mobilephones
as medical devices in mental disorder treatment: an overview. Personal and Ubiquitous Computing.
2015; 19(2): p. 335353.
56. Ben-Zeev D, Schueller SM, Begale M, Duffecy J, Kane JM, Mohr DC. Strategies for mHealth Research:
Lessons from 3 Mobile Intervention Studies. Adm Policy Ment Health. 2015; 42(2): p. 157167. doi: 10.
1007/s10488-014-0556-2 PMID: 24824311
57. Aguilera A. Digital technology and mental health interventions:Opportunities and challenges. Arbor.
20154191(771): a210. doi: 10.3989/arbor.2015.771n1012
58. Clough BA, Casey LM. The smart therapist: A look to the future of smartphones and mHealth technolo-
gies in psychotherapy. Professional Psychology-Research and Practice. 2015; 46(3): p. 147153.
59. Torous J,Staples P, Onnela JP. Realizing the Potential of Mobile Mental Health: New Methods for New
Data in Psychiatry. Curr Psychiatry Rep. 2015; 17(8):602. doi: 10.1007/s11920-015-0602-0 PMID:
26073363
Systematic Review of CBT and BA Apps for Depression
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 18 / 19
60. Saeb S, Zhang M, Karr CJ, Schueller SM, Corden ME, Kording KP, Mohr DC. Mobile phone sensor cor-
relates of depressive symptom severity in daily-life behavior: An exploratory study. J Med Internet Res.
2015; 17(7):e175. doi: 10.2196/jmir.4273 PMID: 26180009
61. Warmerdam L, Riper H, Klein M, van den Ven P, Rocha A, Ricardo Henriques M, et al.Innovative ICT
solutions to improve treatment outcomes for depression: The ICT4Depression project. Stud Health
Technol Inform. 2012; 181:33943. PMID: 22954884
62. Rozbroj T, Lyons A, Pitts M, Mitchell A, Christensen H.Assessing the applicability of e-therapies for
depression, anxiety, and other mood disorders among lesbians and gay men: Analysis of 24 web- and
mobile phone-based self-help interventions. J Med Internet Res. 2014; 16(7): p. 144154.
63. Ben-Zeev D, Scherer EA, Wang R, Xie H, Campbell AT. Next-generation psychiatric assessment:
Using smartphone sensors to monitor behavior and mental health. Psychiatr Rehabil J. 2015; 38(3): p.
218226. doi: 10.1037/prj0000130 PMID: 25844912
64. Kauer S, Reid S, Crooke A, Khor A, Patton G, Jorm A, Jackson H. Emotional self-awareness: Prelimi-
nary analyses of a RCT using a cellular phone self-monitoring program (mobiletype) to decrease early
symptoms of depression. Journal of Adolescent Health. 2011; 48(2): p. S91S92.
65. Callan J.A., et al., CBT mobilework: A technological solution to improve depression outcomes. Clinical
and Translational Science. 4(2): p. 120.
66. Murugan M. Mobile phone based supportive patient care. Indian Journal of Psychiatry. 55: p. S72.
67. Dijk SV,Voshaar RO. Black-Box: Snapshots of daily life time series analysisin a depressed elderly
patient. American Journal of Geriatric Psychiatry. 2014; 22(3): p. S57.
68. Carmi L, Zohar J. Secondary prevention in depression, bipolar and addiction via mobile phone.Euro-
pean Neuropsychopharmacology. 2014; 24: p. S734S735.
69. Bowman MA, Neale NV. Focus on clinical practice: Improving the quality of care. Journal of the Ameri-
can Board of Family Medicine. 25(3): p. 263265. doi: 10.3122/jabfm.2012.03.120068 PMID:
22570385
70. Javelot H, Spadazzi A, Weiner L, Garcia S, Gentili C, Kosel M, Bertschy G. Telemonitoring with respect
to mood disorders and information and communication technologies: Overview and presentation of the
PSYCHE project. BioMed Research International Article ID 104658, 12 pages http://dx.doi.org/10.
1155/2014/104658
71. Ly KH, Janni E, Wrede R, Sedem M, Donker T, Carlbring P, Andersson G. Experiences of a guided
smartphone-based behavioral activation therapy for depression: A qualitative study. Internet Interven-
tions. 2015; 2(1): p. 6068.
72. National Institute for Clinical Excellence. Depression: management of depression in primary and sec-
ondary care. Clinical Guideline 23. 2004. http://www.scamfyc.org/documentos/depresion%20NICE.
pdf (accessed 16 June 2015)
73. BinDhim NF. Hawkey A. Trevena L. A systematic review of quality assessment methods for smart-
phone health apps. 2015 Feb; 21(2):97104. doi: 10.1089/tmj.2014.0088 PMID: 25469795
74. Kuehnhausen M, Frost VS. Trusting smartphone apps? To install or not to install, that is the question.
Cognitive Methods in Situation Awareness and Decision Support; IEEE International Multi-Disciplinary
Conference; 2013 Feb 2528; San Diego, CA, USA. IEEE; 2013. pp. 3037.
75. Dimitropoulos L, Patel V, Scheffler SA, Posnack S. Public attitudes in health information exchange:
Perceived benefits and concerns. Am J Manag Care. 2011; 17(12 Spec No.):SP1116. PMID:
22216769
76. Dunnebeil S, Sunyaev A, Blohm I, Leimeister JM, Kromar H. Determinants of physicians technology
acceptance for e-health in ambulatory care. Int J Med Inform. 2012; 81(11):74660. doi: 10.1016/j.
ijmedinf.2012.02.002 PMID: 22397989
77. Agaku IT, Adisa AO, Ayo-Yusuf OA, Connolly GN. Concern about security and privacy, and perceived
control over collection and use of health information are related to withholding of health information
from healthcare providers. J Am Med Inform Assoc.2014; 21(2):3748. doi: 10.1136/amiajnl-2013-
002079 PMID: 23975624
78. U.S. Food and Drug Administration. Guidance for industry and food and drug administration staff- mobile
medical applications. 2013. Available:http://www.fda.gov/downloads/MedicalDevices/.../UCM263366.
pdf on.
Systematic Review of CBT and BA Apps for Depression
PLOS ONE | DOI:10.1371/journal.pone.0154248 May 2, 2016 19 / 19
... Evidence-based recommendations have suggested that cancer survivors should have access to specialized care to address the broad range of their survivorship needs, and Cancer Care Ontario included psychosocial needs as a priority in its recent standardized guidelines for follow-up of cancer survivors [12][13][14][15]. Studies have found that cognitive behavioral therapy (CBT) can be an efficacious intervention for anxiety and depression; however, the research is limited on its effect on FOR [16][17][18][19]. CBT has been described as encompassing a broad range of treatments which focus primarily on cognitive techniques to change unhelpful thinking patterns, beliefs, attitudes, and behaviors [20]. ...
... Traditionally, CBT is designed and delivered as a structured program or series of sessions, involving in-person interactions with a therapist or psychologist. However, the need for more accessible treatment has prompted the mental health system to consider other models of service delivery (i.e., Internetbased) that provide timely support and that are less laborintensive for patients and less of a burden on the healthcare system [17,18]. Cancer programs and clinics that provide post-treatment and survivorship care are also integrating more support and expertise to address the psychosocial needs of cancer survivors, further indicating the importance of accessible services and resources [21][22][23]. ...
... The PHQ-9 is a validated multipurpose instrument for screening, diagnosing, monitoring, and measuring the severity of depression [24]. Scores are calculated as a summation and classified as minimal depression (0-4), mild level of depression (5-9), moderate level of depression (10)(11)(12)(13)(14), moderately severe depression (15)(16)(17)(18)(19), and severe depression (20 or greater). The GAD-7 provides a brief assessment of generalized anxiety, with the following scoring system: minimal anxiety 0-4, mild level of anxiety 5-9, moderate level of anxiety 10-14, and severe anxiety 15-21 [25]. ...
Article
Full-text available
Background Depression, anxiety, and fear of recurrence (FOR) are prevalent among cancer survivors, and it is recommended that they have access to supportive services and resources to address psychosocial needs during follow-up care. This study examined the impact of a virtual cognitive behavioral therapy (CBT)-based telephone coaching program (BounceBack®) on depression, anxiety, and FOR. Method Through the After Cancer Treatment Transition (ACTT) clinic at the Women’s College Hospital (Toronto, Canada), eligible participants were identified, consented, and referred to the BounceBack® program. Program participation involved completion of self-selected online workbooks and support from trained telephone coaches. Measures of depression (PHQ-9), anxiety (GAD-7), and FOR (fear of cancer recurrence inventory, FCRI) were collected at pre-intervention (baseline) and post-intervention (6-month and 12-month time points). For each psychosocial measure, paired t-tests compared mean scores between study time points. Participant experiences and perceptions were collected through a survey. Results Measures of depression and anxiety significantly improved among participants from pre-intervention to post-intervention. Scores for PHQ-9 and GAD-7 decreased from moderate to mild levels. Measure of FOR also significantly improved, while FCRI sub-scale scores significantly improved for 5 of the 7 factors that characterize FOR (triggers, severity, psychological distress, functional impairment, insight). Participants rated the intervention a mean score of 7 (out of 10), indicating a moderate level of satisfaction and usefulness. Conclusion This study suggested that a virtual CBT-based telephone coaching program can be an effective approach to managing depression, anxiety, and fear of recurrence in cancer survivors.
... In contrast, research-developed apps prioritize evidence-based content and rigorous trials, which slows widespread availability as it can take 2 decades from conceptualization to public dissemination [37,42], leaving a gap between research-tested and commercially available mTDIs. Indeed, recent reviews of the content of >10,000 purported mental health apps in the commercial marketplace have noted that a vast majority have not gone through rigorous intervention development and testing [43] and are lacking or inconsistent with evidence-based psychotherapy principles [44], including apps specifically targeting youth mental health [40,45]. Although research trials have demonstrated promising findings for some mTDIs, the heterogeneity and poor quality of certain mTDIs and studies have led to inconclusive evidence across outcomes [46], which makes the role of these interventions in mental health services less clear [47]. ...
Article
Background Rates of mental health problems among youth are high and rising, whereas treatment seeking in this population remains low. Technology-delivered interventions (TDIs) appear to be promising avenues for broadening the reach of evidence-based interventions for youth well-being. However, to date, meta-analytic reviews on youth samples have primarily been limited to computer and internet interventions, whereas meta-analytic evidence on mobile TDIs (mTDIs), largely comprising mobile apps for smartphones and tablets, have primarily focused on adult samples. Objective This study aimed to evaluate the effectiveness of mTDIs for a broad range of well-being outcomes in unselected, at-risk, and clinical samples of youth. Methods The systematic review used 5 major search strategies to identify 80 studies evaluating 83 wellness- and mental health-focused mTDIs for 19,748 youth (mean age 2.93-26.25 years). We conducted a 3-level meta-analysis on the full sample and a subsample of the 38 highest-quality studies. Results Analyses demonstrated significant benefits of mTDIs for youth both at posttest (g=0.27) and follow-up (range 1.21-43.14 weeks; g=0.26) for a variety of psychosocial outcomes, including general well-being and distress, symptoms of diverse psychological disorders, psychosocial strategies and skills, and health-related symptoms and behaviors. Effects were significantly moderated by the type of comparison group (strongest for no intervention, followed by inert placebo or information-only, and only marginal for clinical comparison) but only among the higher-quality studies. With respect to youth characteristics, neither gender nor pre-existing mental health risk level (not selected for risk, at-risk, or clinical) moderated effect sizes; however, effects increased with the age of youth in the higher-quality studies. In terms of intervention features, mTDIs in these research studies were effective regardless of whether they included various technological features (eg, tailoring, social elements, or gamification) or support features (eg, orientation, reminders, or coaching), although the use of mTDIs in a research context likely differs in important ways from their use when taken up through self-motivation, parent direction, peer suggestion, or clinician referral. Only mTDIs with a clear prescription for frequent use (ie, at least once per week) showed significant effects, although this effect was evident only in the higher-quality subsample. Moderation analyses did not detect statistically significant differences in effect sizes based on the prescribed duration of mTDI use (weeks or sessions), and reporting issues in primary studies limited the analysis of completed duration, thereby calling for improved methodology, assessment, and reporting to clarify true effects. Conclusions Overall, this study’s findings demonstrate that youth can experience broad and durable benefits of mTDIs, delivered in a variety of ways, and suggest directions for future research and development of mTDIs for youth, particularly in more naturalistic and ecologically valid settings.
... CBT is recommended as a first-line treatment for depression and anxiety [34]. iCBT is becoming increasingly common in high-income countries [35] for many reasons including insufficient psychotherapists trained in CBT, the advantages in terms of accessibility (convenience in schedule and travel), empowerment (increased sense of control and psychological ownership), and reduction of barriers (reduced cost, logistical barriers, wait times, and discretion for those who are embarrassed to seek help). Several iCBT programs, both guided and unguided with and without modules to address comorbid disorders, have been developed, implemented, and evaluated (for reviews, see [36][37][38]). ...
Article
Full-text available
Background Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are highly prevalent among university students and predict impaired college performance and later life role functioning. Yet most students do not receive treatment, especially in low-middle-income countries (LMICs). We aim to evaluate the effects of expanding treatment using scalable and inexpensive Internet-delivered transdiagnostic cognitive behavioral therapy (iCBT) among college students with symptoms of MDD and/or GAD in two LMICs in Latin America (Colombia and Mexico) and to investigate the feasibility of creating a precision treatment rule (PTR) to predict for whom iCBT is most effective. Methods We will first carry out a multi-site randomized pragmatic clinical trial ( N = 1500) of students seeking treatment at student mental health clinics in participating universities or responding to an email offering services. Students on wait lists for clinic services will be randomized to unguided iCBT (33%), guided iCBT (33%), and treatment as usual (TAU) (33%). iCBT will be provided immediately whereas TAU will be whenever a clinic appointment is available. Short-term aggregate effects will be assessed at 90 days and longer-term effects 12 months after randomization. We will use ensemble machine learning to predict heterogeneity of treatment effects of unguided versus guided iCBT versus TAU and develop a precision treatment rule (PTR) to optimize individual student outcome. We will then conduct a second and third trial with separate samples ( n = 500 per arm), but with unequal allocation across two arms: 25% will be assigned to the treatment determined to yield optimal outcomes based on the PTR developed in the first trial (PTR for optimal short-term outcomes for Trial 2 and 12-month outcomes for Trial 3), whereas the remaining 75% will be assigned with equal allocation across all three treatment arms. Discussion By collecting comprehensive baseline characteristics to evaluate heterogeneity of treatment effects, we will provide valuable and innovative information to optimize treatment effects and guide university mental health treatment planning. Such an effort could have enormous public-health implications for the region by increasing the reach of treatment, decreasing unmet need and clinic wait times, and serving as a model of evidence-based intervention planning and implementation. Trial status IRB Approval of Protocol Version 1.0; June 3, 2020. Recruitment began on March 1, 2021. Recruitment is tentatively scheduled to be completed on May 30, 2024. Trial registration ClinicalTrials.gov NCT04780542 . First submission date: February 28, 2021.
... However, CBT is a complex treatment that requires extensive training to be able to competently deliver a high degree of treatment fidelity. Consequently, there is considerable and persistent unmet need for effective psychological depression treatments because of a chronic shortage of trained therapists [32]. To address the unmet needs for treatment, there has been considerable interest in "third-wave psychological interventions", such as Behavioural Activation (BA). ...
Article
Full-text available
Mental health issues are at the forefront of healthcare challenges facing contemporary human society. These issues are most prevalent among working-age people, impacting negatively on the individual, his/her family, workplace, community, and the economy. Conventional mental healthcare services, although highly effective, cannot be scaled up to address the increasing demand from affected individuals, as evidenced in the first two years of the COVID-19 pandemic. Conversational agents, or chatbots, are a recent technological innovation that has been successfully adapted for mental healthcare as a scalable platform of cross-platform smartphone applications that provides first-level support for such individuals. Despite this disposition, mental health chatbots in the extant literature and practice are limited in terms of the therapy provided and the level of personalisation. For instance, most chatbots extend Cognitive Behavioural Therapy (CBT) into predefined conversational pathways that are generic and ineffective in recurrent use. In this paper, we postulate that Behavioural Activation (BA) therapy and Artificial Intelligence (AI) are more effectively materialised in a chatbot setting to provide recurrent emotional support, personalised assistance, and remote mental health monitoring. We present the design and development of our BA-based AI chatbot, followed by its participatory evaluation in a pilot study setting that confirmed its effectiveness in providing support for individuals with mental health issues.
... Mobile apps are a novel way to deliver therapy programs on mobile devices and share similarities to web-based or computer-based therapy programs. Over 2200 mobile apps claim to deliver therapy for several mental health conditions but lack rigorous validation, are not necessarily based on therapeutic principles, are gamified and addictive, or harm recovery [1,43,74,154,180,185,187,[194][195][196]198,200,231,237,280,308,333,356,368]. Furthermore, 38% of trials for mobile apps were uncontrolled ( Table 2). ...
Article
Full-text available
Background: The COVID-19 pandemic has shifted mental health care delivery to digital platforms, videoconferencing, and other mobile communications. However, existing reviews of digital health interventions are narrow in scope and focus on a limited number of mental health conditions. Objective: To address this gap, we conducted a comprehensive systematic meta-review of the literature to assess the state of digital health interventions for the treatment of mental health conditions. Methods: We searched MEDLINE for secondary literature published between 2010 and 2021 on the use, efficacy, and appropriateness of digital health interventions for the delivery of mental health care. Results: Of the 3022 records identified, 466 proceeded to full-text review and 304 met the criteria for inclusion in this study. A majority (52%) of research involved the treatment of substance use disorders, 29% focused on mood, anxiety, and traumatic stress disorders, and >5% for each remaining mental health conditions. Synchronous and asynchronous communication, computerized therapy, and cognitive training appear to be effective but require further examination in understudied mental health conditions. Similarly, virtual reality, mobile apps, social media platforms, and web-based forums are novel technologies that have the potential to improve mental health but require higher quality evidence. Conclusions: Digital health interventions offer promise in the treatment of mental health conditions. In the context of the COVID-19 pandemic, digital health interventions provide a safer alternative to face-to-face treatment. However, further research on the applications of digital interventions in understudied mental health conditions is needed. Additionally, evidence is needed on the effectiveness and appropriateness of digital health tools for patients who are marginalized and may lack access to digital health interventions.
Article
Much effort has been invested in designing digital systems that keep people ‘hooked’. By contrast, comparatively little is known about how designers can support people in re-gaining control. Online, however, hundreds of apps and browser extensions promise to help people self-regulate use of digital devices. Reviews and popularity metrics for these digital self-control tools (DSCTs) can indicate which design patterns are useful in the wild. Moreover, they reveal how platforms like Android and iOS differ in the ecosystems they enable for DSCTs, which has important implications for end users. We analysed reviews, installation numbers, and ratings for 334 DSCTs on the Google Play, Chrome Web, and Apple App stores, investigating what user reviews reveal about usage contexts and key design challenges, and how functionality relates to popularity metrics. Our thematic analysis of 1,529 reviews (sampled from a data set of 53,978 distinct reviews scraped in March 2019) found that DSCTs are seen as highly important for focusing on less instantly rewarding tasks when digital distractions are easily available. Users seek DSCTs that adapt to their personal definitions of distraction, and provide support that is sufficient to change behaviour without feeling too coercive. Reviewers suggested combining design patterns to provide a level of support that is ‘just right’. This was mirrored in the ratings, where tools combined different types of design patterns (e.g., website blocking and goal reminders) tended to receive higher ratings than those implementing a single type. We discuss implications for research and design, including how design patterns in DSCTs interact, and how psychological reactance to DSCTs can be reduced.
Preprint
Full-text available
Effective treatments for depression exist, yet access to evidence-based care remains limited. Mobile health (mHealth) apps offer one avenue for improving access. We describe the development of RuminAid, a mHealth app that uses evidence-based techniques. This intervention targets a single key depressogenic process—rumination. Its behavioral strategies are rooted in empirically-supported interventions for depression and rumination. RuminAid is consistent with recommendations by mHealth researchers and integrates “gamified” elements. The goal of RuminAid is to teach users to increase awareness of ruminative episodes, deploy behavioral strategies to replace habitual rumination, and address rumination-related deficits in attention. An initial focus-group study found that the consumer-rated quality of a storyboard version of RuminAid was in the acceptable to good range. Participants endorsed overwhelming positive beliefs about the perceived impact of RuminAid. Namely, 95% of participants reported that they believe RuminAid will help depressed ruminators with depression and rumination. Results highlighted a need for improved app aesthetics (eg, a more appealing color scheme and modern design). Subsequent modifications based on focus-group feedback and future directions are discussed.
Article
Full-text available
This review summarizes the current meta-analysis literature on treatment outcomes of CBT for a wide range of psychiatric disorders. A search of the literature resulted in a total of 16 methodologically rigorous meta-analyses. Our review focuses on effect sizes that contrast outcomes for CBT with outcomes for various control groups for each disorder, which provides an overview of the effectiveness of cognitive therapy as quantified by meta-analysis. Large effect sizes were found for CBT for unipolar depression, generalized anxiety disorder, panic disorder with or without agoraphobia, social phobia, posttraumatic stress disorder, and childhood depressive and anxiety disorders. Effect sizes for CBT of marital distress, anger, childhood somatic disorders, and chronic pain were in the moderate range. CBT was somewhat superior to antidepressants in the treatment of adult depression. CBT was equally effective as behavior therapy in the treatment of adult depression and obsessive-compulsive disorder. Large uncontrolled effect sizes were found for bulimia nervosa and schizophrenia. The 16 meta-analyses we reviewed support the efficacy of CBT for many disorders. While limitations of the meta-analytic approach need to be considered in interpreting the results of this review, our findings are consistent with other review methodologies that also provide support for the efficacy CBT.
Article
Full-text available
This study assessed pilot feasibility and validity of a mobile health (mHealth) system for tracking mood-related symptoms after traumatic brain injury (TBI). A prospective, repeated measures design was used to assess compliance with daily ecological momentary assessments (EMA) conducted via a smartphone application over an 8-week period. An mHealth system was developed specifically for individuals with TBI and utilized previously validated tools for depressive and anxiety symptoms (Patient Health Questionnaire-9, Generalized Anxiety Disorder-7). Feasibility was assessed in 20 community-dwelling adults with TBI via an assessment of compliance, satisfaction and usability of the smartphone applications. The authors also developed and implemented a clinical patient safety management mechanism for those endorsing suicidality. Participants correctly completed 73.4% of all scheduled assessments, demonstrating good compliance. Daily assessments took <2 minutes to complete. Participants reported high satisfaction with smartphone applications (6.3 of 7) and found them easy to use (6.2 of 7). Comparison of assessments obtained via telephone-based interview and EMA demonstrated high correlations (r = 0.81-0.97), supporting the validity of conducting these assessments via smartphone application in this population. EMA conducted via smartphone demonstrates initial feasibility among adults with TBI and presents numerous opportunities for long-term monitoring of mood-related symptoms in real-world settings.
Article
Full-text available
Mobile phones are ubiquitous in society and owned by a majority of psychiatric patients, including those with severe mental illness. Their versatility as a platform can extend mental health services in the areas of communication, self-monitoring, self-management, diagnosis, and treatment. However, the efficacy and reliability of publicly available applications (apps) have yet to be demonstrated. Numerous articles have noted the need for rigorous evaluation of the efficacy and clinical utility of smartphone apps, which are largely unregulated. Professional clinical organizations do not provide guidelines for evaluating mobile apps. Guidelines and frameworks are needed to evaluate medical apps. Numerous frameworks and evaluation criteria exist from the engineering and informatics literature, as well as interdisciplinary organizations in similar fields such as telemedicine and healthcare informatics. We propose criteria for both patients and providers to use in assessing not just smartphone apps, but also wearable devices and smartwatch apps for mental health. Apps can be evaluated by their usefulness, usability, and integration and infrastructure. Apps can be categorized by their usability in one or more stages of a mental health provider's workflow. Ultimately, leadership is needed to develop a framework for describing apps, and guidelines are needed for both patients and mental health providers.
Article
Full-text available
Background Depression is a common, burdensome, often recurring mental health disorder that frequently goes undetected and untreated. Mobile phones are ubiquitous and have an increasingly large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms. Objective The objective of this study was to explore the detection of daily-life behavioral markers using mobile phone global positioning systems (GPS) and usage sensors, and their use in identifying depressive symptom severity. MethodsA total of 40 adult participants were recruited from the general community to carry a mobile phone with a sensor data acquisition app (Purple Robot) for 2 weeks. Of these participants, 28 had sufficient sensor data received to conduct analysis. At the beginning of the 2-week period, participants completed a self-reported depression survey (PHQ-9). Behavioral features were developed and extracted from GPS location and phone usage data. ResultsA number of features from GPS data were related to depressive symptom severity, including circadian movement (regularity in 24-hour rhythm; r=-.63, P=.005), normalized entropy (mobility between favorite locations; r=-.58, P=.012), and location variance (GPS mobility independent of location; r=-.58, P=.012). Phone usage features, usage duration, and usage frequency were also correlated (r=.54, P=.011, and r=.52, P=.015, respectively). Using the normalized entropy feature and a classifier that distinguished participants with depressive symptoms (PHQ-9 score ≥5) from those without (PHQ-9 score
Article
Full-text available
Background: Major depression is one of the most debilitating diseases in terms of quality of life. Less than half of patients suffering from depression can achieve remission after adequate antidepressant treatment. Another promising treatment option is cognitive-behavior therapy (CBT). However, the need for experienced therapists and substantive dedicated time prevent CBT from being widely disseminated. Methods/design: A multi-center randomized trial is currently being conducted since September 2014. The smartphone-based CBT program, named the "Kokoro-App," for major depression has been developed and its feasibility has been confirmed in a previous open study. The program consists of an introduction, 6 sessions and an epilogue, and is expected to be completed within 9 weeks by patients. In the present trial, 164 patients with DSM-5 major depressive disorder and still suffering from depressive symptoms after adequate antidepressant treatment for more than 4 weeks will be allocated to the Kokoro-App plus switching antidepressant group or the switching antidepressant alone group. The participants allocated to the latter group will receive full components of the Kokoro-App after 9 weeks. Discussion: An effective and reachable intervention may not only lead to healthier mental status among depressed patients, but also to reduced social burden from this illness. This paper outlines the background and methods of a trial that evaluates the possible additive value of a smartphone-based CBT program for treatment-resistant depression. Trial registration: UMIN-CTR: UMIN000013693. (registered on 1 June 2014).
Article
center (15% and 17% respectively). The three preferred sources of information about parenting and adolescent preventive health were providers, the internet and monthly newsletters. Parents' actual sources of information varied by socio-demo-graphics. Parents who were less educated were more likely to obtain information via the TV while those who were more educated were more likely to obtain information via the inter-net. Parents who were more educated were also more likely to want to receive information via the internet. AAs were less interested in receiving information via technological sources (CD/DVD, video, text messaging, e-mail). Conclusions: More parents wanted their child's provider to give them information about parenting and adolescent preventive health than received it. Both direct providers delivered counseling as well as print and multi-media-based tools for educating parents of adolescents about parenting and adolescent preventive health are acceptable. Technology-based options represent new approaches for increasing parental capacity to promote adolescent preventive health, but acceptability varies by race and parent education level. Purpose: School-based health centers (SBHC) have proven to be a useful way to provide screening and health services for adolescents. Recent national recommendations have suggested the annual screening of this population for depression and suicidal ideation. This study looks at screening adolescents for depression and suicide ideation in an inner city middle school SBHC in a Hispanic neighborhood in the northeastern part of the United States. The study explored the utility of opportunistic depression screening with the Reynolds Adolescent Depression Scale, Version 2 (RADS-2). Methods: Students visiting the SBHC completed (self-administered) the RADS-2. All screens were scored by a mental health provider. Adolescents with positive screens were linked to mental health services. Demographics, RADS-2 scores and follow-up data were maintained in excel databases and analyzed with SPSS version 15.0. Results: Three hundred sixty-nine adolescents were screened: 55% females, 75% Hispanic, 22% African American, 2% other, ranging in age between 11 and 16 (mean age: 13.7 (/ 0.9). Fifty-seven adolescents (15.4%) had a positive RADS-2 score and 25(6.8%) were identified as positive by clinical judgment. Fifty-seven African American adolescents were screened, 10 (14.9%) being positive, 8 females (22.9%) and 2 males (6.3%). One-hundred eighty-two Hispanic adolescents were screened, 44 (19.5%) being positive, 26 (19.4%) females and 18 (19.6%) males. Forty-five (79%) adolescents positive for depression were enrolled in therapy with the SBHC psychologist with a mean number of therapeutic sessions per adolescent of 16.7 (range: 1-37). Thirty-seven adolescents with positive screens were not enrolled in clinical services: 25 (67%) received services at another sites, 8 (22%) were lost to follow-up, and 4 (11%) were determined not to be at risk at follow-up. Conclusions: The school based health care center served as an excellent site for screening for depression and suicidal ide-ation. Not only does this site provide easy access to the adolescent population, but it also gives good access for follow-up for those adolescents identified as being at risk. This study also shows a significant number of young Hispanic adolescent males at risk for depression and suicide, more than generally would have been predicted for this population. Purpose: Depression is a severe, recurrent condition that affects many people worldwide. Sixty percent of people who have one depressive episode will have another. In addition, up to 30% of young people experience depressive symptoms by 18 years of age. While these symptoms may be mild initially, many progress to moderate and then severe symptoms of depression. To date, no effective strategies exist that curtail the progression of mild depression to major depressive disorder. Effective early intervention programs need to be low cost and easy to use whilst also retaining the interest of young people. Self-monitoring has potential as an early intervention tool for young people, particularly when cell phones are used as a medium. Self-monitoring is a simple technique often used in behavioral therapy to increase awareness about mood and stressful events. In turn, emotional self-awareness is likely to decrease symptoms of depression. Previous qualitative research indicates that self-monitoring via cell phones increases emotional self-awareness in five ways: awareness of feelings, the ability to identify these feelings, communication of emotions to others, understanding the context of emotions (causes and consequences) and decision-making regarding emotions. This RCT investigates (i) whether self-monitoring increases young people's awareness of their moods and reduces depres-sive symptoms and (ii) whether emotional self-awareness mediates the relationship between self-monitoring and depres-sive symptoms. S91 Poster Abstracts / 48 (2011) S18 –S120
Article
Errors in Byline, Author Affiliations, and Acknowledgment. In the Original Article titled “Prevalence, Severity, and Comorbidity of 12-Month DSM-IV Disorders in the National Comorbidity Survey Replication,” published in the June issue of the ARCHIVES (2005;62:617-627), an author’s name was inadvertently omitted from the byline on page 617. The byline should have appeared as follows: “Ronald C. Kessler, PhD; Wai Tat Chiu, AM; Olga Demler, MA, MS; Kathleen R. Merikangas, PhD; Ellen E. Walters, MS.” Also on that page, the affiliations paragraph should have appeared as follows: Department of Health Care Policy, Harvard Medical School, Boston, Mass (Drs Kessler, Chiu, Demler, and Walters); Section on Developmental Genetic Epidemiology, National Institute of Mental Health, Bethesda, Md (Dr Merikangas). On page 626, the acknowledgment paragraph should have appeared as follows: We thank Jerry Garcia, BA, Sara Belopavlovich, BA, Eric Bourke, BA, and Todd Strauss, MAT, for assistance with manuscript preparation and the staff of the WMH Data Collection and Data Analysis Coordination Centres for assistance with instrumentation, fieldwork, and consultation on the data analysis. We appreciate the helpful comments of William Eaton, PhD, Michael Von Korff, ScD, and Hans-Ulrich Wittchen, PhD, on earlier manuscripts. Online versions of this article on the Archives of General Psychiatry Web site were corrected on June 10, 2005.
Article
Objective: Cultural and health service obstacles affect the quality of pregnancy care that women from vulnerable populations receive. Using a participatory design approach, the Stress in Pregnancy: Improving Results with Interactive Technology group developed specifications for a suite of eHealth applications to improve the quality of perinatal mental health care. Materials and methods: We established a longitudinal participatory design group consisting of low-income women with a history of antenatal depression, their prenatal providers, mental health specialists, an app developer, and researchers. The group met 20 times over 24 months. Applications were designed using rapid prototyping. Meetings were documented using field notes. Results and discussion: The group achieved high levels of continuity and engagement. Three apps were developed by the group: an app to support high-risk women after discharge from hospital, a screening tool for depression, and a patient decision aid for supporting treatment choice. Conclusion: Longitudinal participatory design groups are a promising, highly feasible approach to developing technology for underserved populations.