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Title
Digital Technology and Mental Health Interventions: Opportunities and Challenges

Permalink
https://escholarship.org/uc/item/0nj447nk

Journal
Arbor, 191(771)

ISSN
0210-1963

Author
Aguilera, Adrian

Publication Date
2015-02-28

DOI
10.3989/arbor.2015.771n1012

Peer reviewed

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University of California
 ARBOR Ciencia, Pensamiento y Cultura
Vol. 191-771, enero-febrero 2015, a210 | ISSN-L: 0210-1963
doi: http://dx.doi.org/10.3989/arbor.2015.771n1012

ESPAÑA Y ESTADOS UNIDOS. PERSPECTIVAS SOBRE EL TRABAJO SOCIAL Y EL BIENESTAR SOCIAL /

SPAIN AND USA. SOCIAL WORK AND SOCIAL WELFARE PERSPECTIVES

DIGITAL TECHNOLOGY TECNOLOGÍA DIGITAL E

AND MENTAL HEALTH INTERVENCIONES PARA
INTERVENTIONS: OPPORTUNITIES LA SALUD MENTAL:
AND CHALLENGES OPORTUNIDADES Y BARRERAS
Adrian Aguilera
University of California, Berkeley
School of Social Welfare
aguila@berkeley.edu

Citation/Cómo citar este artículo: Aguilera, A. (2015). “Digital Copyright: © 2015 CSIC. This is an open-access article distributed
Technology and Mental Health Interventions: Opportunities under the terms of the Creative Commons Attribution-Non
and Challenges”. Arbor, 191 (771): a210. doi: http://dx.doi. Commercial (by-nc) Spain 3.0 License.
org/10.3989/arbor.2015.771n1012

Received: January 16, 2014. Accepted: July 25, 2014.

ABSTRACT: The growth of the Internet, mobile phones, social RESUMEN: El crecimiento del Internet, los teléfonos móviles,
media and other digital technologies has changed our world in las redes sociales y otras tecnologías digitales ha cambiado
many ways. It has provided individuals with information that nuestro mundo de muchas maneras. Ha proporcionado a las
was previously only available to a select few. An example of personas con la información que antes sólo estaba disponible
the reach of technology is data that as of October 2012, there para un grupo selecto, por ejemplo a partir de octubre de 2012.
are over 6 billion phones worldwide (BBC, 2012). The availa- Un ejemplo del alcance de la tecnología son los datos que dicen
bility of data in real time has presented hopes of intervening que hay más de 6 millones de teléfonos en todo el mundo
more efficiently and managing health problems by leveraging (BBC, 2012). La disponibilidad de los datos en tiempo real a
limited human resources. It also has an impact in changing the presentado la esperanza de intervenir de manera más eficiente
roles of providers and patients and in legal and ethical issues y manejar los problemas de salud los recursos humanos
including privacy in digital health interactions. This paper will limitados. También tiene un impacto en el cambio de los roles
discuss why digital technology has received recent attention in de los proveedores y los pacientes y en aspectos legales y éticos,
the area of mental health, present some applications of tech- incluyendo la privacidad en las interacciones de salud digital.
nology for mental health to date, explore the challenges to full Este artículo discutirá unas razones por cual la tecnología digital
implementation in clinical settings, and present future oppor- ha recibido atención recientemente en el área de salud mental,
tunities for digital technologies. presentará algunas aplicaciones de la tecnología para mejorar
la salud mental hasta la fecha, explorará algunas barreras para
la diseminación en la práctica clínica, y presentará algunas
oportunidades futuras de las tecnologías digitales.

KEYWORDS: digital health; mobile health; mental health; PALABRAS CLAVE: salud digital; salud móvil; salud mental;
technology; intervention. tecnología; intervención.
 WHY TECHNOLOGY? tive ways of improving the quality and reach of psy-
chotherapies using innovative digital technologies
Digital technologies, including the Internet, mo-
such as mobile phones and smartphones in particular
a210 bile applications, sensors and others have the poten-
(Boschen, 2009; Kazdin and Blase, 2011).
tial to improve the delivery of health interventions.
These technologies are expected to enhance patient Digital technologies also have the potential to speed
Digital Technology and Mental Health Interventions: Opportunities and Challenges

outcomes by increasing the reach of existing inter- the intervention development pipeline and bring evi-
ventions and by utilizing new technologies to mea- dence-based interventions to populations in need at a
sure health behaviors. Digital technology can allow faster pace. In the last 10-20 years, there has been a
for real time data collection and result in immediate push to develop and implement clinical interventions
clinical interventions based on that data (Morris and that are evidence based. However, even after such in-
Aguilera, 2012). These technologies offer hope to fill terventions are developed, they take a long time to
gaps in improving clinical interventions in social work get disseminated. The Institute of Medicine reported
and related professions. In particular, the growth of that on average, 20% of clinical trials result in changes
mobile phone applications for health has grown very in healthcare and those take 17 years to get to people
rapidly and provides opportunities to expand current that could benefit from them (Institute of Medicine,
care beyond the traditional clinic setting. 2001). Furthermore, when they are available, they
are not always accessible to everyone in need; par-
While there is a large discrepancy in home Internet
ticularly those from low-income backgrounds who
access between high and low income households in
may not be able to afford quality care as well as indi-
the US, this is not the case with mobile phone use as
viduals in rural areas who do not have physical access
more Americans own a mobile phone than a com-
to trained clinicians. In cases where interventions are
puter (Lenhart, 2010). The use of digital technology
disseminated, fidelity to the original evidence based
in health applications is experiencing a tremendous
treatment varies when they are applied to complex
boom in large part as a result of ballooning health
service settings, which differ from controlled lab set-
care costs. There is much excitement but there is still
tings where treatments are often developed. If inter-
limited data on the impact of technology aside from
ventions morph over time, new questions arise about
evidence for antiretroviral medication adherence and
whether adaptations and changes might lessen ef-
smoking cessation (Free et al., 2013). Although it is
ficacy and effectiveness. Finally, patients and clients
unclear whether technology will plug all of the holes
in community based health and mental health set-
in the American healthcare system, technology is be-
tings tend to have lower attendance and completion
ing applied rapidly and thus it is imperative to study
rates for behavioral treatments (Miranda et al., 1996;
the effects of integrating digital technologies into
Miranda et al., 2003). Digital technologies may help
health interventions. There are many factors that limit
improve the rates of studies that go “to market” and
the effectiveness of traditional health interventions in
reduce the time that it takes to get there. For exam-
the US that could be addressed by the implementa-
ple, if an intervention is automated, there is little to
tion of technological solutions. Two of those factors
change when going from a research trial to a clinical
that hinder efficient clinical intervention include lim-
service thereby making it almost immediately avail-
ited human resources and slow dissemination of evi-
able for dissemination and often requires less human
dence based treatments.
resources.
Slow Dissemination
Limited Workforce
For health and mental health care, digital technolo-
Assuming the previously mentioned problems of ef-
gies may allow clinicians to reach difficult to reach
ficient development and implementation of behavior-
populations. For example, using video streaming,
al interventions did not exist, there are still limitations
these technologies could facilitate access to specialist
to the number of people in need that trained (and
services in communities with a low supply of trained
non-trained) individuals can reach. The reason for
providers or limited resources, including bilingual bi-
this is that the efforts to implement behavioral inter-
cultural providers. In the area of mental health, tra-
ventions are inherently consumable; that is the time
ditional psychotherapy relies on a one-to-one treat-
that people put into providing therapy, giving advice,
ment model which inherently limits the number of
or managing cases can never be regained (Muñoz,
people any individual therapist can help in a lifetime.
2010). Digital technologies make these interventions
As a result, there have been calls to develop innova-

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more scalable and potentially more sustainable over always on, portable, low cost and ubiquitous while
time. Additionally, even with medication based inter- being able to send and receive various forms of data
ventions, those medications, once consumed cannot including audio, text, and photos. Smartphones add
a210
benefit anyone else. However, technology can act as connections to the Internet, programmable apps,
a force multiplier by helping disseminate digital infor- video, as well as location and movement tracking

Adrian Aguilera
mation to large numbers of people. For example, one (Boschen and Casey, 2008). These features are desir-
could develop an automated online evidence based able for health interventions as they address the chal-
behavioral treatment for depression and anxiety, lenges of reaching individuals where they are and col-
which provides access to almost unlimited numbers of lecting accurate real time data as opposed to relying
people at a time without degrading the quality of the on retrospective reports. Instead of asking patients,
intervention. The next section will provide examples how much they have exercised in the past week or
of interventions that have utilized technology to im- month, they could be sent that question via text mes-
prove the delivery of health interventions. sage on a daily basis or better yet, their phones could
track their movement throughout the week.
WHAT HAS BEEN DONE? The interest in mobile devices stems in part from
realizing the potential of automated technologies us-
Online interventions ing online interventions paired with the potential for
Digital technologies have been involved in clini- higher participation using mobile devices that people
cal practice for years but are only recently reaching carry with them at all times. As consumers become
the mainstream of practice, although at a slow pace. more accustomed to using mobile technology in their
Web-based interventions have the most evidence of daily lives, they are increasingly interested in gain-
efficacy when compared with other technologies. The ing access to information via mobile phones. For ex-
Internet boom of the early 2000s brought with it de- ample, in an Australian sample, 76% of people were
velopment of health interventions online due to the interested in using mobile phones for mental health
low cost, increase in convenience and stigma reduc- monitoring and self-management. Importantly, peo-
tion (Griffiths et al, 2006). Online interventions be- ple with mental health symptoms were more inter-
gan as passive websites with information that people ested in using such services, suggesting that there is
would read and complete much like a self help book indeed an untapped market of consumers of mental
and have evolved into more interactive sites that are health information that could benefit from psycho-
customized to individuals. Online interventions have logical interventions outside of the traditional tools of
addressed a wide range of problems including depres- psychotherapy practice (Proudfoot et al., 2010). In an-
sion (Christensen, Griffiths and Korten, 2002), smok- other study, 98% of low income mostly unemployed
ing (Muñoz et al., 2006), obesity (Williamson et al., clients in outpatient substance abuse treatment were
2006; Gold et al., 2007), diabetes (Glasgow, Boles, interested in using interactive text messaging to help
Mckay, Feil and Barrera Jr, 2003), and alcohol con- them maintain sobriety (Muench et al., 2013). Re-
sumption (Murray et al., 2007) among others. These views have highlighted that mobile interventions are
interventions have largely been shown to be effica- well accepted by end-users and welcome additions
cious, however there are still major limitations. Pri- to physical and mental health treatments (Cole-Lew-
marily, web based interventions suffer from high rates is and Kershaw, 2010; Head et al., 2013; Heron and
of attrition -- many people who start are not likely to Smyth, 2010). These interventions have the benefit
complete the interventions. In an open intervention of ongoing contact and assessment beyond the tradi-
as many as 85% of participants fail to come back for a tional therapeutic environment.
second “session” of treatment (Eysenbach, 2005). The research base for mobile technology applica-
tions is growing but is still limited in terms of specific
mHealth applications that can be delivered on a broad scale.
Mobile phones have garnered much attention in Although much research has been done using mobile
recent years spawning the area of mHealth (mo- phone based text messaging (SMS) based on cognitive
bile health) due to the ubiquity of phones world- behavioral therapy (CBT) principles, most of these ef-
wide as well as other characteristics inherent to the forts have been limited to feasibility studies. Evidence
technology. Standard mobile phones are attractive based smartphone applications using behavioral and
for behavioral health interventions because they are cognitive methods tend to be in developmental stages.

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 These include a Dialectical Behavior Therapy (DBT) app reinforce the group therapy session for that week.
for borderline personality disorder, (Rizvi et al., 2011) a For example, messages will ask patients to reflect on
mobile therapy application using CBT techniques (Mor- helpful and unhelpful thoughts for the day during the
a210
ris et al., 2010), and an Internet and mobile interven- focus on thoughts and mood. On other days they re-
tion for depression using context sensing to identify ceive other queries (e.g. about daily activities, social
Digital Technology and Mental Health Interventions: Opportunities and Challenges

emotional states and intervene appropriately (Burns interactions, and sleep patterns) to reinforce the con-
et al., 2011). Although these applications require a nection between mood states and thoughts, activities,
broader evidence base before they are ready for imple- and social interactions that are central to the therapy.
mentation, they foretell future applications for moving This adjunct is designed to deepen self-awareness and
psychotherapy beyond the one on one encounter. knowledge of the themes that are discussed in therapy,
but it has also resulted in increased perceived social
Text messaging is a basic feature of mobile phones,
support. For example, patients have mentioned that
but one that can be deployed for messaging around
when they receive a message, they feel that “someone
health. Text messaging is perhaps the most widely
cares about my health” even though messages are au-
available mobile tool available on nearly all phones
tomated. This text messaging system is an example of
that can be utilized by clinicians and their entire patient
technology extending human capacity and strengthen-
population. There is more research on text messaging
ing an intervention that is known to be efficacious.
than any other mobile format with the most promising
results being for appointment adherence and smoking There are also numerous mobile phone applications
cessation (Free et al., 2013). Text messages can make that attempt to provide overlapping therapeutic ser-
change goals more salient in one’s natural environ- vices highlighting CBT theory. Some applications have
ment because they automatically push therapeutic been developed in clinical settings and take principles
content rather than relying on the client to proactively of CBT and transfer them to a mobile device very spe-
open an application which reduces the likelihood that cifically. The Veterans Administration, in particular,
clients will be able to ignore change goals in the face has embraced the use of technology and has devel-
of unhealthy environment triggers. There are several oped applications based on evidence based practices
simple free messaging services in which clinicians can designed for veterans but available to all. One such
send one time or repeated reminders to a patient at application is the PTSD Coach available on iPhone and
a specific day and time. There are also more interac- Android platforms. The application targets the man-
tive text messaging systems that can be utilized in con- agement of PTSD with four modules focused on 1)
junction with therapeutic goals. For example, the US education, 2) self-assessment, 3) symptom manage-
federal government has released “smokefreetxt” a free ment, and 4) social support. The application is target-
SMS program for individuals attempting to quit smok- ed towards vets but can likely be used by others deal-
ing. Additionally, SMS can be used to trigger mobile ing with PTSD. While these applications have not been
web applications and cloud based audio and video files directly tested, they were developed based on empiri-
when clients need extra support. Most phones also cally supported interventions and principles providing
have MMS capability where pictures of a loved one or a some foundation for their use in clinical practice.
visual goal can be sent to patients at specific times. Not
As noted above other intervention apps have been
only do these applications help patients make goals
developed for borderline personality disorder (Rizvi et
more salient and track progress, but they can be used
al., 2011), depression using context sensing to iden-
as simple appointment and homework reminders.
tify emotional states and intervene appropriately
One example of a text messaging application is an (Burns et al., 2011), and substance abuse using GPS to
adjunct to group cognitive behavioral therapy for de- trigger reminders when someone may be entering an
pression developed by Aguilera and Muñoz (2011). areas previously associated with substance use (Gus-
This system is designed to send messages to individu- tafson et al., 2011). Additionally, there are numerous
als currently in therapy asking them what their mood applications that target specific components of CBT
is at a random time during the day. This mood data is and may compliment practice such as programs that
received by clinicians and can be displayed back to pro- target gratitude, meditation, guided relaxation, thera-
vide discussion points for negative and positive mood peutic breathing and increasing positive emotion.
states throughout the week along with correlates and However, like all self-guided change programs the
coping strategies. In addition to sending mood moni- question becomes, how many times will a person use
toring messages, patients also receive messages to an application without therapist support?

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Passive Sensing WHAT ARE THE BARRIERS?
The most rapidly growing area of mobile health
that is being embraced by the general public and can Privacy and HIPAA
a210
have tremendous utility to clinicians are ambulatory There are many opportunities and immense po-
personal physiological, activity and location sensing tential for digital technologies to improve health and

Adrian Aguilera
tools (Intille, 2007), which will build on some of the mental health service provision but there are many
examples presented earlier. Many promising appli- challenges to overcome as well. For example some
cations are those that passively monitor a range of patients/clients simply may not like using technology
stimuli from activity level to heart rate or galvanic mobile phones. Other issues include concerns about
skin response using external sensors. The most intrusiveness and the invasion of privacy (Proudfoot
basic level of mobile sensing utilizes technologies et al., 2010). While these barriers may hinder adop-
built into modern smartphones such as accelerom- tion, understanding them may also help to facili-
eters, gyroscopes, and GPS. A popular application tate adoption. For example, Muench and colleagues
of these technologies is the detection of movement (2013) found that 40% of individuals interested in us-
and location. It may soon be common practice for ing text messaging for continuing care around prob-
therapists to monitor the activity level of patients lems of addiction preferred NOT having any messages
through smart phone accelerometers and GPS. With that specifically reference drug use in the text. These
available applications today you can monitor the ac- findings sensitize us to the importance of research on
tivity level of depressed patients and see whether how to individualize applications to suit client prefer-
they expanded their behavioral repertoires by en- ences and needs. By no means are mobile applica-
gaging in new pleasant activities through GPS and tions a panacea, but they are a potentially powerful
even mapping. Sleep disturbances associated with a tool to increase the impact of interventions that we
range of diagnoses can be monitored with various know work when applied properly.
apps and inexpensive devices that track and graph
In addition to challenges from patients and clients,
sleep through activity monitoring features in smart
therapists are sometimes reluctant to implement the
phones. These applications can be combined with
technology. One of the primary concerns in the use
self-monitoring to help understand the daily corre-
of technology for health is the security of data and
lates of poor sleep.
potential privacy breaches. In the U.S., technologies
A benefit of mobile phones is the ability to cap- must be compliant with HIPAA (Health Insurance Por-
ture objective data and nowhere is this more pro- tability and Accountability Act), a law enforced by the
nounced than with psychophysiological assessment. Department of Health and Human services to protect
While biomonitoring typically requires additional identifiable health information. The act is meant to
hardware, which includes a sensor, we can now re- protect consumers, but it has had a negative effect
view periods when our clients were most aroused on innovation due to the fears of violating privacy and
or hypervigilant with objective data and help them being at risk for lawsuits. Large health organizations
prepare for these situations when clear patterns are have received substantial monetary penalties for hav-
taking place. These applications will increasingly ing systems that allowed access to patient data. The
include intervention components such as notifica- risk of lawsuits has made many large healthcare or-
tions when an individual is aroused (e.g. through ganizations hesitant to implement technological in-
galvanic skin response, heart rate variability or novations into care due to the fear of exposing data.
heart rate) to engage in stress management tech- Fines range from thousands of dollars up to a $4.3
niques or as noted earlier, alerts based on global million dollar penalty issued to Cignet Health (U.S.
positioning or geographic information to avoid high Department of Health and Human Services, 2013).
risk situations (e.g. drug use areas) as is currently These fears are particularly pronounced because most
being implemented through The Comprehensive people in healthcare do not have expertise in the data
Health Enhancement Support System (CHESS™) de- management technologies necessary to ensure priva-
veloped through the University of Wisconsin (Gus- cy. Furthermore, even when steps are taken to ensure
tafson et al., 2011). This system integrates human privacy, determined hackers may still be able to access
services and computer technology to help individu- such data.
als manage health problems by providing accessible
On the private sector or developer side, companies
and personalized information and support.
are reluctant to develop digital health tools due to the

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 regulations that come with working with patient data Research Designs
and large healthcare system record systems. As a re-
Gold standard research designs such as randomized
sult the majority of applications and devices that are
a210 controlled trials (RCTs) are slow and not always com-
being developed by private companies are targeted to
patible with ever changing technologies. The process
consumers and not to health care providers or orga-
of research and testing is slow compared to the devel-
Digital Technology and Mental Health Interventions: Opportunities and Challenges

nizations. This is problematic because those who are

opment of new technologies. For example in the U.S.,
the most motivated to download the latest app or pur-
large scale studies that are funded by National Insti-
chase a monitoring device are likely to be better off and
tutes of Health grants typically take 1-2 years in the
in better health than those who may not afford or do
grant preparation, submission, and approval process,
not know about such options. Because of policy barri-
5 years to conduct the study, and 1-2 years to publish
ers, both real and perceived private companies are not
the results. The typical 7-11 year time span from grant
addressing larger health problems nor integrating into
submission to publication of results will see a variety
clinical systems due at least in part to liability concerns.
of changes in technologies that may make the results
There are steps that can be taken to limit privacy much less relevant than they were when a research
breaches such as restricting the transfer of sensitive idea was initially being developed. As an example, the
information, using code words, ensuring the informa- last 7-11 years saw the proliferation of text messaging,
tion is secure on a single phone, using passwords and development of the iPhone and Android mobile phones
deleting messages. Protocols that are in place to ad- and accompanying applications for each of those plat-
dress crises should remain in place and do not need forms. If one conceived of a technology based inter-
to be supplanted by the use of technology. It is im- vention 7-11 years ago, it probably would not have
portant to inform patients about how to use technol- included any of these now ubiquitous technologies.
ogy and that the use of mobile technology does not Therefore, funding organizations might consider expe-
necessarily mean that a therapist will be available or dited reviews and shorter timelines for reviews.
will be monitoring messages at all times. Other safety
Also, the preferred RCT design may not always be
measures could include integrating a safety protocols
the best choice in testing an intervention due to the
such as texting the word “HELP” to receive informa-
length of time they require as well as the rigidity of
tion about a suicide hotline and instructions on going
design. As noted in recent reviews on mobile inter-
to the emergency room (Aguilera and Muñoz, 2011).
ventions (Kumar et al., 2013; Mohr et al., 2013; Riley
There is also the concern of increased therapist time
et al., 2013), the technology is advancing so quickly
commitment if one is constantly connected to clients
that research cannot keep up with development.
via technology. However, boundaries can be set up
Other ways of assessing impact besides RCTs, includes
similar to boundaries used in current evidence based
continuously evaluating interventions as they are de-
treatments regarding phone or email contact.
veloped (Mohr et al., 2013) and incorporating novel
Clinicians and health professionals are also some- research designs that can take advantage of advanced
times concerned that technology is simply duplicating statistical methods.
their efforts and will result in reduced need for them
or in less treatment seeking by potential clients. That Digital Divide
thinking fails to recognize that technological applica-
Despite the ubiquity of many technologies, there are
tions can be used to enhance care. Even with technol-
still barriers to access some of these digital technolo-
ogy, personal contact and real-time intervention and
gies. For example, in the US, Mobile phones are widely
feedback will still be required to treat most individuals
available and owned, but there is variability in terms
seeking in-person services. Stepped care models are
of who has broadband internet access in their home,
alredy using these technologies as first line treatments
with lower income and education as well as increased
and there are simply not enough individual therapists
age being related to lower connectivity at home (Zick-
to address the unmet need for mental health prob-
hur and Smith, 2013). As of May 2013, 56% of people
lems in the U.S. and globally. Furthermore, the appli-
using mobile phones owned a smartphone, however
cation of technology is improved when combined with
ownership is also largely related to income, education
a live, trained support (Mohr, Cuijpers and Lehman,
and age. If exposure to technology is variable, then
2011). Although technology holds potential promise,
interventions developed on technology platforms
most research indicates that uptake of technological
may reach variable audiences as well. For example,
solutions is aided greatly by human relationships.
an iPhone app could be developed to help treat and

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track a mental health problem such as depression or or well organized. Another problem with the large
PTSD but people who do not own those phones will amounts of data that can be generated from digital
not use the technology. This is particularly concerning health technologies is the interoperability of various
a210
since income and education are also related to higher applications that may be collecting data and feeding
incidences of many health and mental health prob- into electronic health records. Applications are often

Adrian Aguilera
lems that these technologies are targeting (Aguilera et in different formats and have difficulty “speaking to
al., 2012). The type of smartphone that one owns is each other” so that data can be integrated.
also related to income level with higher earners more
The sheer quantity of data can pose problems for
likely to own iPhones. These are important consider-
storing large data files as well as making sense of what
ations when developing an intervention that is meant
the data mean. For example, a passive activity or lo-
to reach large numbers of people in high need.
cation monitor could be collecting data constantly
Strategies to counter effects of the digital divide but not all of that data may be useful for determining
include developing interventions with the most ubiq- health outcomes. Analytic techniques and algorithms
uitous yet limited technologies (e.g. text messaging, are necessary to pull out the “signal from the noise.”
mobile voice calls) or providing individuals with no This is the type of data overload that health systems
or low cost access to advanced technologies (e.g. and providers are concerned will take up more of their
smartphones and sensor devices). The strategy of time and efforts than is being saved by increased ef-
using existing technologies is likely to result in more ficiencies in data collection.
sustainability but is limited in the potential positive
impact compared to more advanced technologies. On Interdisciplinarity
the other hand, the use of sophisticated smartphones
Solving crucial problems such as determining what
and sensors have potential for collecting more accu-
data to capture and picking out the most important
rate data and thus resulting in more personalized in-
pieces of information requires experts from multiple
tervention. As the cost of smartphones and sensors
disciplines. A major challenge in this area of work is
decreases, many more individuals will have access to
that technologists (computer scientists, engineers,
these technologies and in turn newer interventions.
etc.) and clinicians/researchers on the health side
However, there will always be a group of people,
have different expertise and often different goals.
mostly from low-income backgrounds that will lag in
During the development phases of technologies and
adoption of technology. Therefore researchers such
interventions, academics from both sides of the field
as Aguilera and Muñoz (2011) have focused on tech-
are often speaking to different audiences with differ-
nologies that are the most ubiquitious, such as mobile
ent goals. The technology side is tasked with devel-
phone based text messaging in order to create the
oping new technologies that are often years away
most scalable and sustainable intervention possible.
from reaching real use while health researchers are
concerned with technologies that can be tested and
Big data
implemented with patients today, not years from
The availability of large quantities of real time data now. One example of the different goals is the fact
can be both a blessing and a curse for patients, provid- that both sides publish articles in different scholarly
ers and health care systems. More data can help bet- journals that have vastly different formats and re-
ter assess what is occurring in people’s daily lives and quirements. These “separate worlds” keep both sides
help determine how that relates to illness and well- of the digital health field from truly integrating.
ness. Having access to real time data from multiple
applications and/or sensor systems might also allow WHAT’S NEXT?
for targeting interventions when individuals actually
Digital health technologies initially experienced a
need them. For example, if a clinician can determine
heightened level excitement and expectations. How-
when a patient is feeling depressed or is at risk, a sys-
ever, development and testing have not occurred
tem that is tracking relevant data might alert the cli-
as rapidly as was imagined, possibly due to the dif-
nician so that a targeted outreach is made. This type
ficulties of collaboration between the technology and
of system could be applied to assertive community
health camps. Insurance companies have been hesi-
treatment programs that intensively work with the
tant to fund most of these newer uses of technology,
neediest clients. However, these data could easily be
possibly because data on their effectiveness are not
overwhelming to clinicians if they are not relevant
well established. We are likely heading down from the

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ARBOR Vol. 191-771, enero-febrero 2015, a210. ISSN-L: 0210-1963 doi: http://dx.doi.org/10.3989/arbor.2015.771n1012
 “peak of inflated expectations” and hopefully mov- The future of health information technology will be
ing soon into a more productive period where people best served by allowing for multiple ways of accessing
are not extolling the virtues of technology as much data and information. These technologies will soon be
a210
as they are attempting to solve the problems posed able to provide intelligent just-in-time interventions
when technology meets health. The future of digital based on real-time data capture, which have the po-
Digital Technology and Mental Health Interventions: Opportunities and Challenges

health technologies includes building a solid evidence tential to help us understand and intervene with cli-
base for interventions using current technologies, and ents on an exponentially different level. However, en-
the development of interventions utilizing cutting thusiasm for the potential of digital health should not
edge technology that is not yet as pervasive. lead to using untested applications or abandoning the
empirical process to improve the application of these
One area of future development will likely include
technologies in an evidence-based manner.
a variety of small sensors that measure biometric and
environmental data, which can then be sent to comput-
ing systems that can detect events that need targeted CONCLUSION
intervention. One might imagine a patient with seri- There are calls to develop innovative ways of improv-
ous mental illness streaming data to providers and that ing the quality and reach of efficacious clinical inter-
data is then deciphered for instances that are triggering ventions using innovative tools such as mobile phones
symptoms or functional impairment. Resources could and smartphones (Kazdin and Blase 2011; Boschen,
then be targeted more accurately to address specific 2009). The use of digital technology in health appli-
problems that are based on real time data and not only cations is experiencing a tremendous boom in large
self report. Many technology applications and sensors part as a result of ballooning health care costs and the
in the future will be based on ideas that have thus far limitations of one on one therapy to meet the mental
only existed in science fiction novels. One example is an health needs of the population. In particular, mobile
electrochemical tatoo that is in development to sense phone applications for health have grown very rapidly
real time biometric data (Jia et al., 2013). Data result- which provides opportunities to expand current care
ing from this sensor could then trigger interventions beyond the traditional clinic setting. While there is a
or notifications to healthcare providers. Another novel large discrepancy in home Internet access between
sensor is a microchip that is embedded into medica- high and low income households in the US, this is not
tion and once ingested, sends a signal to a patch on the the case with mobile phone use as more Americans
user’s arm which transmits data to a mobile phone. An own a mobile phone than a computer (Lenhart, 2010).
application could provide information regarding next There are many challenges to the continued develop-
dosage and could send data to healthcare providers to ment of digital health technologies but with flexibility,
inform them about medication adherence. This pill sen- collaboration and empirical testing we can work to en-
sor is a good example of a technology that while useful sure that the public receives the benefit of tools that
also presents serious concerns about invasion of privacy can help improve health and lower the cost curve.
and coercive treatments. This battle between effective
interventions and privacy concerns is likely to continue
and will lead to many ethical debates and dilemmas.

REFERENCES
Aguilera, A., Leykin, Y., Adler, N. and Mu- BBC (2012). UN: Six billion mobile phone Burns, M. N., Begale, M., Duffecy, J., Gergle,
ñoz, R. F. (2012). Assessing the impact subscriptions in the world. Retrieved D., Karr, C. J., Giangrande, E. and Mohr,
of relative social position and absolute 9/30/2013, 2013, from http://www. D. C. (2011). Harnessing context sens-
community resources on depression bbc.co.uk/news/technology-19925506 ing to develop a mobile intervention
and obesity among smokers. Ameri- for depression. Journal of Medical Inter-
Boschen, M. J. (2009). Mobile telephones and
can Journal of Community Psychol- net Research, 13, 3, e55. http://dx.doi.
psychotherapy: I. capability and applicabil-
ogy, 50, 1-2, pp. 211-216. http://dx.doi. org/10.2196/jmir.1838
ity. Behavior Therapist, 32, 8, pp. 168-175.
org/10.1007/s104640129492z
Christensen, H., Griffiths, K. M. and Korten,
Boschen, M. J. and Casey, L. M. (2008). The
Aguilera, A. and Muñoz, R. F. (2011). Text A. (2002). Web-based cognitive be-
use of mobile telephones as adjuncts
messaging as an adjunct to CBT in low- havior therapy: Analysis of site usage
to cognitive behavioral psychotherapy.
income populations: A usability and fea- and changes in depression and anxi-
Professional Psychology: Research and
sibility pilot study. Professional Psychol- ety scores. Journal of Medical Inter-
Practice, 39, 5, pp. 546-552. http://
ogy: Research and Practice, 42, 6, p. 472. net Research, 4, 1, e3. http://dx.doi.
dx.doi.org/10.1037/07357028.39.5.546
http://dx.doi.org/10.1037/a0025499 org/10.2196/jmir.4.1.e3

8
ARBOR Vol. 191-771, enero-febrero 2015, a210. ISSN-L: 0210-1963 doi: http://dx.doi.org/10.3989/arbor.2015.771n1012
Cole-Lewis, H. and Kershaw, T. (2010). Heron, K. E. and Smyth, J. M. (2010). Ecolog- in predominantly low-income young mi-
Text messaging as a tool for behavior ical momentary interventions: Incorpo- nority women. JAMA: The Journal of the
change in disease prevention and man- rating mobile technology into psychoso- American Medical Association, 290, 1,
agement. Epidemiologic Reviews, 32, cial and health behaviour treatments. pp. 57-65. http://dx.doi.org/10.1001/ a210
pp. 56-69. http://dx.doi.org/10.1093/ British Journal of Health Psychol- jama.290.1.57
epirev/mxq004 ogy, 15, 1, pp. 1-39. http://dx.doi.
Mohr, D., Cuijpers, P., and Lehman, K.

Adrian Aguilera
org/10.1348/135910709X466063
Eysenbach, G. (2005). The law of attrition. (2011). Supportive accountability: A
Journal of Medical Internet Research, Institute of Medicine (2001). Crossing the model for providing human support to
7, 1, p. e11. http://dx.doi.org/10.2196/ quality chasm: A new health care sys- enhance adherence to eHealth inter-
jmir.7.1.e11 tem for the 21st century. ventions. Journal of Medical Internet
Research, 13, 1, e30. http://dx.doi.
Free, C., Phillips, G., Galli, L., Watson, L., Fe- Intille, S. S. (2007). Technological innova-
org/10.2196/jmir.1602
lix, L., Edwards, P., Patel, V. and Haines, tions enabling automatic, context-
A. (2013). The effectiveness of mobile- sensitive ecological momentary as- Mohr, D. C., Cheung, K., Schueller, S. M., Hen-
health technology-based health behav- sessment. In Stone, A., Shiffman, S., dricks Brown, C., and Duan, N. (2013).
iour change or disease management Atienza, A. and Nebeling, L. The Science Continuous evaluation of evolving be-
interventions for health care consum- of Real-Time Data Capture. Self-Reports havioral intervention technologies.
ers: A systematic review. PLoS Medi- in Health Research. Oxford: Oxford Uni- American Journal of Preventive Medi-
cine, 10, 1, e1001362. http://dx.doi. versity Press, pp. 308-337. cine, 45, 4, pp. 517-523. http://dx.doi.
org/10.1371/journal.pmed.1001362 org/10.1016/j.amepre.2013.06.006
Jia, W., Bandodkar, A. J., Valdes-Ramirez, G.,
Glasgow, R. E., Boles, S. M., Mckay, H. G., Windmiller, J. R., Yang, Z., Ramirez, J., Morris, M. E., Kathawala, Q., Leen, T. K.,
Feil, E. G. and Barrera Jr, M. (2003). Chang, G. and Wang, J. (2013). Electro- Gorenstein, E. E., Guilak, F., Labhard,
The D-net diabetes self-management chemical tattoo biosensors for real-time M. and Deleeuw, W. (2010). Mobile
program: Long-term implementa- noninvasive lactate monitoring in hu- therapy: Case study evaluations of a cell
tion, outcomes, and generalization re- man perspiration. Analytical Chemistry, phone application for emotional self-
sults. Preventive Medicine, 36, 4, pp. 85, 14, pp. 6553-6560. http://dx.doi. awareness. Journal of Medical Inter-
410-419. http://dx.doi.org/10.1016/ org/10.1021/ac401573r net Research, 12, 2, e10. http://dx.doi.
S00917435(02)000567 org/10.2196/jmir.1371
Kazdin, A. E. and Blase, S. L. (2011). Re-
Gold, B. C., Burke, S., Pintauro, S., Buzzell, booting psychotherapy research and Morris, M. E. and Aguilera, A. (2012). Mo-
P., and Harvey Berino, J. (2007). Weight practice to reduce the burden of men- bile, social, and wearable computing
loss on the web: A pilot study compar- tal illness. Perspectives on Psycho- and the evolution of psychological prac-
ing a structured behavioral intervention logical Science, 6, 1, 21. http://dx.doi. tice. Professional Psychology: Research
to a commercial program. Obesity, 15, org/10.1177/1745691610393527 and Practice, 43, 6, 622-626. http://
1, 155-164. http://dx.doi.org/10.1038/ dx.doi.org/10.1037/a0029041
Kumar, S., Nilsen, W. J., Abernethy, A.,
oby.2007.520
Atienza, A., Patrick, K., Pavel, M., Riley, Muench, F., Weiss, R. A., Kuerbis, A. and
Griffiths, F., Lindenmeyer, A., Powell, J., W. T., Shar, A. Spring, B., Sprujit-Metz, Morgenstern, J. (2013). Developing a
Lowe, P. and Thorogood, M. (2006). D., Hedeker, D., Honavar, V., Kravitz, theory driven text messaging interven-
Why are health care interventions de- R., Lefevre, R. C., Mohr, D. C., Murphy, tion for addiction care with user driven
livered over the internet? A systematic S. A., Quinn, C., Shusterman, V. and content. Psychology of Addictive Be-
review of the published literature. Jour- Swendeman, D. (2013). Mobile health haviors, 27, 1, 315-321. http://dx.doi.
nal of medical Internet research, 8, 2. technology evaluation: The mHealth org/10.1037/a0029963
http://dx.doi.org/10.2196/jmir.8.2.e12 evidence workshop. American Jour-
Muñoz, R. F. (2010). Using evidence-based
http://dx.doi.org/10.2196/jmir.8.2.e10 nal of Preventive Medicine, 45, 2, pp.
internet interventions to reduce health
228-236. http://dx.doi.org/10.1016/j.
Gustafson, D. H., Shaw, B. R., Isham, A., Bak- disparities worldwide. Journal of Medi-
amepre.2013.03.017
er, T., Boyle, M. G. and Levy, M. (2011). cal Internet Research, 12, 5, e60.
Explicating an evidence-based, theo- Lenhart, A. (2010). Adults, cell phones and
Muñoz, R. F., Lenert, L. L., Delucchi, K.,
retically informed, mobile technology- texting. Retrieved 9/28, 2010, from
Stoddard, J., Perez, J. E., Penilla, C.
based system to improve outcomes for http://pewresearch.org/pubs/1716/
and Pérez-Stable, E. J. (2006). To-
people in recovery for alcohol depen- adults-cell-phones-text-messages
ward evidence-based internet in-
dence. Substance use & Misuse, 46, 1,
Miranda, J., Azocar, F., Organista, K. C., terventions: A spanish/english web
pp. 96-111. http://dx.doi.org/10.3109/
Muñoz, R. F. and Lieberman, A. (1996). site for international smoking cessa-
10826084.2011.521413
Recruiting and retaining low-income tion trials. Nicotine and Tobacco Re-
Head, K. J., Noar, S. M., Iannarino, N. T. and latinos in psychotherapy research. Jour- search, 8, 1, pp. 77-87. http://dx.doi.
Grant Harrington, N. (2013). Efficacy nal of Consulting and Clinical Psychol- org/10.1080/14622200500431940
of text messaging-based interventions ogy, 64, 5, pp. 868-874. http://dx.doi.
Murray, E., McCambridge, J., Khadjesari,
for health promotion: A meta-analysis. org/10.1037/0022006X.64.5.868
Z., White, I. R., Thompson, S. G., God-
Social Science & Medicine, 97, pp. 41-
Miranda, J., Chung, J. Y., Green, B. L., frey, C., Linke, S. and Wallace, P. (2007).
48. http://dx.doi.org/10.1016/j.socs-
Krupnick, J., Siddique, J., Revicki, D. A. The DYD-RCT protocol: An on-line ran-
cimed.2013.08.003
and Belin, T. (2003). Treating depression domised controlled trial of an inter-

9
ARBOR Vol. 191-771, enero-febrero 2015, a210. ISSN-L: 0210-1963 doi: http://dx.doi.org/10.3989/arbor.2015.771n1012
 active computer-based intervention sponsive, relevant (R3) research: A call cy rule violations. Retrieved 9/30, 2013,
compared with a standard information for a rapid learning health research from http://www.hhs.gov/ocr/privacy/
website to reduce alcohol consump- enterprise. Clinical and Translational hipaa/news/cignetnews.html
a210
tion among hazardous drinkers. BMC Medicine, 2, 1, pp. 1-6. http://dx.doi.
Williamson, D. A., Walden, H. M., White, M.
Public Health, 7, 1, p. 306. http://dx.doi. org/10.1186/20011326210
A., York Crowe, E., Newton, R. L., Alfon-
org/10.1186/147124587306
Rizvi, S. L., Dimeff, L. A., Skutch, J., Carroll, so, A., Gordon, S. and Ryan, D. (2006).
Digital Technology and Mental Health Interventions: Opportunities and Challenges

Proudfoot, J., Parker, G., Pavlovic, D. H., D. and Linehan, M. M. (2011). A pilot Two Year Internet Based Randomized
Manicavasagar, V., Adler, E. and Whit- study of the DBT coach: An interactive Controlled Trial for Weight Loss in Afri-
ton, A. (2010). Community attitudes to mobile phone application for individu- can American Girls. Obesity, 14, 7, pp.
the appropriation of mobile phones for als with borderline personality disorder 1231-1243. http://dx.doi.org/10.1038/
monitoring and managing depression, and substance use disorder. Behavior oby.2006.140
anxiety, and stress. Journal of Medical Therapy, 42, 4, pp. 589-600. http://
Zickhur, K. and Smith, A. (2013). Home
Internet Research, 12, 5, e64. http:// dx.doi.org/10.1016/j.beth.2011.01.003
Broadband 2013. Pew Research Center.
dx.doi.org/10.2196/jmir.1475
U.S. Department of Health and Human Ser- Retrieved from: http://www.pewin-
Riley, W. T., Glasgow, R. E., Etheredge, L. vices. (2013). HHS imposes a $4.3 mil- ternet.org/2013/08/26/home-broad-
and Abernethy, A. P. (2013). Rapid, re- lion civil money penalty for HIPAA priva- band-2013/

10
ARBOR Vol. 191-771, enero-febrero 2015, a210. ISSN-L: 0210-1963 doi: http://dx.doi.org/10.3989/arbor.2015.771n1012