2. Bridging the gap:
Smart interventions to overcome obstacles of
traditional cognitive behavioral therapy
By Maurice Niessen
Research manager, Minddistrict
3. Smart interventions 2
Over the past decade and a half, cognitive behavioural therapy
(CBT) based online interventions have been developed for a wide
variety of mental health disorders. Although effective, these in-
terventions have yet to fully address the limitations of traditional
CBT, known as the ‘knowledge to practice gap’ and the ‘therapy
to real world gap’. Recent innovations however suggest that mo-
bile applications will soon overcome these obstacles by delivering
real-time, personalised CBT.
In the past, mental health professionals that offered traditional face-to-face
CBT to their clients had to overcome the difficulties of personalising CBT
protocols to each client, and assisting the client in implementing cognitive
and behavioural strategies in their everyday lives. Nowadays, therapists are
already much better equipped to reduce the gap between the therapist’s
office and real-life. The modern therapist’s toolbox consists of face-to-face
contacts, video conferencing and secure messaging. In addition, websites
and mobile applications are available to deliver psychological educational
material, homework exercises and diaries. Together, these tools have the
potential to make interventions become a seamless part of day-to-day life
enabling clients to access care whenever and wherever they choose.
Real-time data collection
Recently, there has been an increased focus on investigating experiences
outside the therapy office, in the context in which they are occuring. A
powerful rationale for this approach is provided by a growing awareness
that models of psychopathology are dynamic over time and experiences are
situated. The experience sampling method (ESM) is a data collection strate-
gy in which individuals are asked in normal daily life to report their thoughts,
feelings and symptoms, as well as the context (e.g. location, company,
activity) and their judgement of this context. The reports typically have to be
filled out several times a day, on consecutive days, either at random unpre-
dictable moments, at moments signalled by a beeper or alternatively, trigge-
red by an event of interest. The mobile revolution has propelled ESM studies
over recent years and has triggered some to commence developing ESM
based interventions. Amsterdam based Minddistrict is one of these compa-
nies.
4. Smart interventions 3
Minddistrict
In the Netherlands, two out of three mental health care institutions already
apply ehealth in their care provisions or communication with patients. The
vision of Dutch ehealth market leader Minddistrict is to facilitate lasting be-
havioural change by providing effective and cost-effective, seamless ehealth
solutions. For this purpose, Minddistrict has developed an easy-to-use,
secure online platform in which interventions can be dynamically tailored
to the current needs of an individual client. The platform contains eviden-
ce-based CBT modules for the prevention, early intervention, treatment
and aftercare of a wide variety of mental health disorders which can be
implemented with varying levels of professional guidance (self-help, guided
self-help, psychotherapy). In addition to the treatment modules, screening,
secure messaging and video chat can be offered to the client on his or her
personalised platform which is connected to a mobile diary app.
From insight to automated intervention
The ESM is incorporated into Minddistrict’s diary app. Graphs are included to
provide a detailed insight into the daily course of thoughts, feelings, symp-
toms and their context. Displayed in the diary app, the real-time graphs help
to create awareness for the patient. The therapist is able to view the same
graphs in his or her secure online platform. Recent studies suggest that the
ESM can also be utilised to deliver personalised, automated, in-the-mo-
ment, ‘smart’ interventions. Minddistrict agrees with this assessment and
has outlined its three-stage ‘smart’ intervention development plan. At each
progressing stage, increasing levels of intelligence are added to the inter-
vention.
In the first stage, clients complete a brief assessment of their current emoti-
onal status in response to a random sound trigger on Minddistrict’s mobile
diary app with multiple choice touchscreen response options. The respon-
ses determine the nature of the subsequent intervention they will receive.
Supportive messages are displayed in response to reported negative emoti-
ons and reaffirming thoughts are depicted when the client indicates positive
affect. These automated messages have multiple wording variations so that
clients do not encounter the exact same intervention every time, even if
they make similar selections. Also, all intervention content can be accessed
whenever and wherever clients choose.
In addition, in the second stage, correlations between context and emotions
5. Smart interventions 4
are calculated to determine personal protective and risk factors. To create
awareness, these insights are reported to the user and therapist. Emotion
mining, or the automated identification of emotions by analysing patterns
in users’ texts, is utilised for groups of clients who lack the ability to identify
or decribe their emotional state or situational context. Emotion mining may
also allow for subconscious emotions to be addressed and perhaps even
future emotional states to be predicted. Minddistrict is currently studying the
potential of emotion mining in association with Maastricht University.
In the third stage, the flow of realtime assessment data is used to train a
reinforcement learning algorithm that will adapt the frequency, timing,
content and intervention medium to the unique characteristics of the client.
At this stage, algorithms are utilised that would ‘learn’ which momentary
states predict certain behaviours and which mobile interventions influence
these momentary states in the desired direction.
Smart self-management
An algorithm would for instance ‘learn’ that if during the evening, a certain
client assesses his current self-esteem as less than four out of seven, he is
more likely to abuse alcohol and also that a certain audioclip is most likely
to lift his self-esteem. If in addition, analysis indicated that the user is more
likely to experience low self-esteem on a specific day of the week, the
audioclip may be offered early on those evenings as an attempt to avert low
self-esteem. Reinforcement learning could also occur across individuals,
in which an intervention strategy with the highest probability of reward for
each individual is offered, based on an analysis of what worked best for
previous users of the system with similar assessment data.
Because of the multi-media capabilities of mobile devices, the intelligent,
real-time, interventions may consist of text, audio/video clips, photos and
voice recordings, among other media. Although initially offered with profes-
sional guidance, this smart intervention also allows for a greater degree of
self-management by clients.
Minddistrict is seeking alliances with academia to develop this next genera-
tion of online interventions. Will you join us?
6. References
Andersson G, Cuijpers P.Internet-based and other computerized psychological treatments
for adult depression: a meta-analysis.Cogn Behav Ther. 2009;38(4):196-205.
Andrews G, Cuijpers P, Craske MG, McEvoy P, Titov N. Computer therapy for the anxiety
and depressive disorders is effective, acceptable and practical health care: a meta-analysis.
PLoS One. 2010 Oct 13;5(10):e13196.
Cuijpers P, Marks IM, van Straten A, Cavanagh K, Gega L, Andersson G.
Computer-aided psychotherapy for anxiety disorders: a meta-analytic review.
Cogn Behav Ther. 2009;38(2):66-82.
Kelly J, Gooding P, Pratt D, Ainsworth J, Welford M, Tarrier N. Intelligent real-time the-
rapy: harnessing the power of machine learning to optimise the delivery of momentary
cognitive-behavioural interventions. J Ment Health. 2012;21(4):404-14.
Dutch Association of Mental Health and Addiction Care. E-mental Health in the Nether-
lands. 2013.
Ben-Zeev D, Brenner CJ, Begale M, Duffecy J, Mohr DC, Mueser KT. Feasibility, Accepta-
bility, and Preliminary Efficacy of a Smartphone Intervention for Schizophrenia.
Schizophr Bull. 2014 Mar 19. [Epub ahead of print]
Delespaul PAEG. Assessing Schizophrenia in Daily Life. 1995. Maastricht. University of
Maastricht.
Niamat SC. Impact of eMental Health: a Quantitative Analysis. 2011. Amsterdam. Faculty of
Sceinces, Business Mathematics and Informatics, VU University.
Myin-Germeys I, Birchwood M, Kwapil T. From environment to therapy in psychosis: a
real-world momentary assessment approach. Schizophr Bull. 2011 Mar;37(2):244-7.
Myin-Germeys I, Oorschot M, Collip D, Lataster J, Delespaul P, van Os J. Experience
sampling research in psychopathology: opening the black box of daily life. Psychol Med.
2009 Sep;39(9):1533-47.
Remmel F. Emotion Mining. 2014. Maastricht. Department of Knowledge Engineering, Uni-
versity of Maastricht.
Richters J, Gerrits R. Een pilot studie naar de potentiele effecten van online behandeling
voor verschillende angststoornissen en depressie. Gedragstherapie 2013;46:161-178.
Ruwaard J. The Efficacy and Effectiveness of online CBT. 2013. Amsterdam. Department
of Clinical Psychology, University of Amsterdam.
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