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Review
Toward the Design of Evidence-Based Mental Health Information
Systems for People With Depression: A Systematic Literature
Review and Meta-Analysis
Fabian Wahle1*, MSc; Lea Bollhalder2*, MA; Tobias Kowatsch3, PhD; Elgar Fleisch2, PhD
1Center for Digital Health Interventions, Department of Management, Technology and Economics, ETH Zürich, Zürich, Switzerland
2Institute of Technology Management, University of St Gallen, St Gallen, Switzerland
3Center for Digital Health Interventions, Institute of Technology Management, University of St Gallen, St Gallen, Switzerland
*these authors contributed equally
Corresponding Author:
Tobias Kowatsch, PhD
Center for Digital Health Interventions
Institute of Technology Management
University of St Gallen
Dufourstrasse 40a, Buro 1-236
St Gallen, 9000
Switzerland
Phone: 41 712247244
Fax: 41 712247244
Email: tobias.kowatsch@unisg.ch
Abstract
Background: Existing research postulates a variety of components that show an impact on utilization of technology-mediated
mental health information systems (MHIS) and treatment outcome. Although researchers assessed the effect of isolated design
elements on the results of Web-based interventions and the associations between symptom reduction and use of components
across computer and mobile phone platforms, there remains uncertainty with regard to which components of technology-mediated
interventions for mental health exert the greatest therapeutic gain. Until now, no studies have presented results on the therapeutic
benefit associated with specific service components of technology-mediated MHIS for depression.
Objective: This systematic review aims at identifying components of technology-mediated MHIS for patients with depression.
Consequently, all randomized controlled trials comparing technology-mediated treatments for depression to either waiting-list
control, treatment as usual, or any other form of treatment for depression were reviewed. Updating prior reviews, this study aims
to (1) assess the effectiveness of technology-supported interventions for the treatment of depression and (2) add to the debate on
what components in technology-mediated MHIS for the treatment of depression should be standard of care.
Methods: Systematic searches in MEDLINE, PsycINFO, and the Cochrane Library were conducted. Effect sizes for each
comparison between a technology-enabled intervention and a control condition were computed using the standard mean difference
(SMD). Chi-square tests were used to test for heterogeneity. Using subgroup analysis, potential sources of heterogeneity were
analyzed. Publication bias was examined using visual inspection of funnel plots and Begg’s test. Qualitative data analysis was
also used. In an explorative approach, a list of relevant components was extracted from the body of literature by consensus between
two researchers.
Results: Of 6387 studies initially identified, 45 met all inclusion criteria. Programs analyzed showed a significant trend toward
reduced depressive symptoms (SMD –0.58, 95% CI –0.71 to –0.45, P<.001). Heterogeneity was large (I2≥76). A total of 15
components were identified.
Conclusions: Technology-mediated MHIS for the treatment of depression has a consistent positive overall effect compared to
controls. A total of 15 components have been identified. Further studies are needed to quantify the impact of individual components
on treatment effects and to identify further components that are relevant for the design of future technology-mediated interventions
for the treatment of depression and other mental disorders.
(J Med Internet Res 2017;19(5):e191) doi:10.2196/jmir.7381
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KEYWORDS
literature review; mental health; design feature; depression; information systems
Introduction
Over the last decade, numerous technology-mediated treatments
for mental health disorders have been developed and tested in
controlled trials. They form a subset of what the World Health
Organization in 2005 coined “mental health information system”
(MHIS). A MHIS “is a system for collecting, processing,
analyzing, disseminating, and using information about a mental
health service and the mental health needs of the population it
serves” [1]. Although such a system does not necessarily need
to rely on computerization, evidence from recent years suggests
that technology-mediated MHIS holds vast opportunities in
terms of much-needed scalability while ensuring treatment
effectiveness. This was shown by a number of reviews and
meta-analyses on computerized and Internet-delivered MHIS
for mental health disorders in general [2-4] and for depression
in particular [5-8].
Despite this success, it remains unclear what guides the design
of MHIS and the choice of components that support existing
evidence-based mental health interventions. Existing research
postulates a variety of such components that show an impact
on utilization of technology-mediated services and treatment
outcome in general [9]. Morrison et al [10] more specifically
assessed the effect of isolated components on the results of
Web-based MHIS interventions and introduced that “there has
been relatively little formal consideration of how differences in
the design of an intervention (ie, how the content is delivered)
may explain why some interventions are more effective than
others.” They defined four core interactive system components
that may mediate the effects of intervention design on outcomes:
(1) social context and support, (2) contacts with intervention,
(3) tailoring, and (4) self-management. A study by Whitton et
al [11] examined the associations between symptom reduction
and use of components across computer and mobile phone
platforms for people with depression for one specific
computerized intervention. They found that the incorporation
of alert-based components, such as reminders and short
motivational messages, quotes, or facts, that were sent by email
or short message service (SMS) text message showed greater
therapeutic gain compared with programs that did not make use
of these components. At large, it was found that reminders play
a decisive role in the engagement of users in mental health
interventions and are a cost-effective approach for engaging
users [11-13]. In addition, Landenberger and Lipsey [14] studied
the relationship between specific components and the effects
of computerized cognitive behavior therapy (CBT) on the
recidivism of adult and juvenile offenders. Despite these
findings, there remains uncertainty with regard to which
components of technology-mediated interventions for mental
health exert the greatest therapeutic gain across MHIS targeting
people with depression. An analysis reviewing trials of
technology-adaptable interventions for the treatment of
depression in adults with cognitive impairments is still underway
[15].
A recent preliminary literature review by Wahle and Kowatsch
[16] aimed at identifying a first set of generic components for
the design of MHIS for people with depression and acted as a
starting point for this review in further identifying relevant
components. Similar to Morrison et al [10], they hypothesized
that the channel of delivery (eg, mobile phone-based,
Web-based), the degree of peer support, the availability of
subsidiary support, the degree of tailoring, and the existence of
gamification elements likely have an impact on treatment
outcome, independent from the underlying therapeutic approach
[16].
This work aims at extending this list in a systematic manner
and to seek evidence for the effectiveness of each of the newly
identified components. By nature, MHIS represent persuasive
systems. Persuasive systems may be defined as “computerized
software or information systems designed to reinforce, change,
or shape attitudes or behaviors or both without using coercion
or deception” [17]. Therefore, we drew on Oinas-Kukkonnen
and Harjumaa’s generic model of persuasive systems design
[18] to identify further potentially relevant components when
investigating the influence on the effectiveness of MHIS.
Components the authors deemed meaningful that did not strictly
follow Oinas-Kukkonnen and Harjumaa’s proposed system
features were also added.
In summary, this systematic review and meta-analysis aims to
add to the current body of literature by providing a systematic
update in evaluating the overall effectiveness of
technology-mediated treatments for depression, as well as
identifying the current set of system components in use, which
has not previously been conducted on a systematic review
targeting depression treatments.
Methods
An electronic search was conducted in MEDLINE, PsycINFO,
and the Cochrane Library. Titles and abstracts of the identified
randomized controlled trials (RCTs) were screened using
predefined inclusion criteria. We independently assessed the
eligibility for inclusion of all potentially relevant studies
identified by the search strategy. Any disagreements were
resolved by discussion among the authors. Manually screening
reference lists for additional studies of relevance and tracing
trials was aimed to obtain further studies possibly eligible for
inclusion. Included RCTs were categorized by (1) location, (2)
total number of patients randomized, (3) target condition
(depression or depression comorbid with anxiety), (4) depression
severity, (5) age of participants, (6) name and type of
intervention, (7) type of comparator, (8) study quality, and (9)
MHIS system components (see Data Extraction). A change in
validated depression scores was used as the primary outcome.
Data were collected from eligible trials and transferred to a data
extraction table. Study quality was assessed using the widely
used Jadad scale [19], additionally checking each trial for
appropriate randomization, blinding of patients, as well as
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dropouts and withdrawals (see Assessment of Methodological
Quality).
For the implementation of this systematic review, the Preferred
Reporting Items for Systematic Reviews and Meta-Analyses
(PRISMA) statement was used [20]. Methods of the analysis
and inclusion criteria were specified in advance and documented
in a protocol (can be provided on request). However, the
protocol also included the evaluation of other mental disorders.
Due to the large number of RCTs identified and the resulting
high degree of heterogeneity, it was decided that mental
disorders other than depression were not to be evaluated in this
systematic review.
Information Sources
Electronic searches were conducted in MEDLINE, PsycINFO,
and the Cochrane Controlled Trials Register. Medical Subject
Headings (MeSH) and relevant text word terms were used to
identify relevant studies (see Search Strategy). The last search
was run on September 1, 2016. Reference lists of systematic
reviews and articles identified were manually checked for
relevant entries.
Search Strategy
Search terms for depression were used to scan all trials registers
and databases outlined previously. Additional terms for a range
of delivery methods (eg, online, Internet, Web, computer, phone)
and terms that specify the type of intervention (eg, cognitive
behavioral, psychodynamic, interpersonal, psychoeducation)
were applied. Further search terms were utilized to limit the
search to studies of therapeutic interventions (eg, therapy,
psychotherapy, intervention, treatment) and to RCTs. Figure 1
gives an overview of the terms used in this literature search.
As a consequence of the protocol also including the evaluation
of other mental disorders, the search strategy was refined during
the course of the review to limit our study to depression. A
compilation of the preliminarily defined search terms is given
in Figure 1.
Figure 1. Keyword combinations used in the literature search process.
Study Selection
The process of study selection required an eligibility check for
each article identified. Eligibility of studies was assessed by
reviewing the abstracts of the references identified by the search
strategy. Full texts were additionally screened when necessary.
In case of doubt, any disagreements and ambiguous articles
were discussed among the authors, and eligibility of studies was
decided by consensus.
Eligibility Criteria
Type of Study Design
Any parallel-group RCT published in English between January
2000 to September 2016 in a peer-reviewed journal was
considered eligible for inclusion in this systematic analysis and
synthesis.
Type of Participants
Studies were included if they evaluated adults or adolescents
who had any of the following conditions: mild to severe
depression, excluding depression co-occurring with non-
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Diagnostic and Statistical Manual of Mental Disorders (Fifth
Edition) (DSM-V) [21] disorders or depression caused by
environmental factors (eg, traumatic events), or depression
comorbid with anxiety disorders. Studies of participants with
other mental health problems (eg, schizophrenia, sleep disorders,
personality disorders) were excluded to reduce the risk of
heterogeneity.
Types of Interventions
Studies that assessed any form of technology-based intervention
for depression were included in this systematic review. To assure
a sufficient degree of comparability, a necessity for
meta-analyses, we only included interventions in which there
existed evidence for comparable outcomes for the treatment of
depression. This was decided based on literature and
consultation from two licensed psychotherapists. These included
(1) psychotherapy (eg, cognitive behavioral therapy [22],
interpersonal therapy [23], problem-solving therapy [24],
supportive therapy [25]), (2) psychoeducation [26], and (3)
exercise/physical activity [27] which showed results on par with
a pharmacological treatment. Additional administration of drugs
or procedures was allowed. The following channels for service
delivery were considered eligible for inclusion: (1) offline
delivery, including all interventions that did not require an
Internet connectivity to provide care (eg, stand-alone
computers); (2) Web-supported delivery, including interventions
that made use of the Internet to deliver services (eg, using
interactive websites to provide interventions or online self-help
forums); and (3) mobile phone, smartphone, and tablet delivery,
including all treatment programs that made use of mobile phone
or tablet apps. The range of apps ranged from simple message
passing to feature-rich multimedia interventions.
To meet the secondary inclusion criteria, all eligible clinical
trials (according to the aforementioned eligibility criteria) were
then inspected with regard to their technical feasibility. Criteria
for studies being classified as technically feasible were the
following: (1) to provide a methodologically structured format
of care to the participants, the intervention must have adhered
to a manual, protocol, or structured approach (with clearly stated
processes, program structure, and objectives) and (2) treatment
must not have been primarily based on face-to-face interaction,
group discussion, or any other form of treatment that required
personal interaction. Specific accompanying service
configurations, which facilitated interaction and support with
the study team and/or peer groups made possible by technology,
were eligible for inclusion. In general, mental health care
services, such as psychotherapeutic or behavioral interventions,
were deemed suitable for the provision in a guided or nonguided
format and were thus considered technically feasible. Only trials
of interventions that were considered to be technically viable
were included in this systematic review.
Types of Endpoints and Outcome Measures
In terms of types of endpoints, RCTs assessing the impact on
symptoms of depression were taken into consideration. Our
primary outcome measures of interest were symptoms of
depression. Trials were eligible for inclusion if they have
evaluated the severity of depression pre- and postintervention
using one or both of these valid assessment scales: (1) the Beck
Depression Inventory (BDI, BDI-1A, or BDI-II) or (2) the
Patient Health Questionnaire (PHQ, PHQ-9, or PHQ-2).
The BDI is a widely used psychometric test to assess
characteristic attitudes and symptoms of depression. The test
consists of 21 multiple-choice self-report questions and is
employed by the majority of researchers and health care
professionals to measure depression severity [28]. The PHQ is
a brief, self-administered assessment tool for screening and
diagnosis as well as for selecting and monitoring treatment. It
is part of the longer PHQ that integrates DSM-IV depression
criteria with other leading major depressive symptoms into a
concise self-report instrument [29].
Types of Controls
Studies were included if they compared technology-based
interventions for depression to either waiting-list control (WLC),
treatment as usual (TAU), or any other form of treatment for
depression. The RCTs were also deemed eligible if they
compared one channel of service delivery to another channel
of delivery. Trials were further considered eligible if they
analyzed interventions that compared different forms of
subsidiary support.
System Components
For each identified component, we provide a rational for
inclusion in the Results section. Despite some components
appearing to be derived from underlying psychological theory,
they were included because they were either enabled or
administered by technology. For each study included in the
systematic review, we determined the presence of defined
system components for later analysis.
We would like to emphasize that our analysis of system
components is not comprehensive and was only conducted to
the degree possible based on published information in the
respective literature included for the meta-review.
Data Extraction
Data collection tables were predeveloped and subsequently
refined during the process of data extraction. The following
information were collected from every article.
Comorbidity
The occurrence of comorbidity was recorded. A difference was
made between depression/depressive symptoms only and
depression/depressive symptoms co-occurring with anxiety.
Characteristics of Intervention
The name of the therapy program and the year and location of
the study were recorded. Also, information on the duration of
the intervention in weeks and follow-up in months was collected.
In addition, program structure and format, as well as the number
of modules, were recorded. For each intervention, we further
gathered any information on the aim of the intervention (inferred
from the description of the intervention) and the MHIS
channel(s) used (eg, online, mobile phone, computer program).
Characteristics of the Control Condition
Where applicable, all relevant information provided on the
control condition was recorded.
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Sample Characteristics
After applying the inclusion and exclusion criteria, we collected
information on the severity of clinical depression at baseline.
As a consequence of differences in the reporting of symptom
severity, which was used for the inclusion/exclusion of
participants, we categorized studies into one of four severity
classes.
Similarly, we recorded age inclusion and exclusion criteria of
all studies included in this systematic review and categorized
studies into five groups (see Table 1). All data on the total size
of the study population as well as on the number of participants
in the treatment and control group(s) were collected.
Table 1. Categorization of studies according to baseline depression severity and age of participants.
RatingItem
Depression severity
PHQ-9
No depression0
Minimal depression1-5
Mild depression6-9
Moderate depression10-14
Moderately severe15-19
Severe depression≥20
BDI-II
No depression0-13
Minimal depression14-19
Mild depression20-28
Severe depression≥29
Depression category
Not reported0
Mild/minimal to moderate1
Moderately severe/severe2
Moderately severe to severe/major depressive episode3
Age category
No age restrictions0
Adults (>16 years of age)1
Adolescents (14-24 years of age)2
Older adults (>50 years of age)3
Adults without older adults (18-75 years of age)4
All Relevant Outcomes
We recorded all relevant outcomes reported on at least one of
the following scores: the BDI or PHQ score.
Study Quality
Study quality was assessed according to Jadad et al [19] (see
Assessment of Methodological Quality).
Service Components
For a quantified overview, the individual system components
were either binary coded or, if applicable, kept in original scale.
Assessment of Methodological Quality
The quality of trials was examined according to the Jadad score
[19] and use of intention-to-treat analysis for the available
endpoints and practice of a blinded endpoint assessment. Further
information can be obtained from the protocol and the quality
assessment table (see Table 2).
Table2. Study quality: risk of bias in included studies (N=45).
(1) Double blinded? (2) Withdrawals and dropouts reported?
(3) Method of randomization reported and appropriate? (4)
Method of blinding reported and appropriate? (5) Analysis
“intention-to-treat”? (6) Assessment of the endpoint blinded?
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Table 2.
Quality ratingTotal score654321Study
Depression
Good5.00.51.00.51.01.00.0Agyapong
[30]
Good5.00.51.00.51.01.00.0Andersson
[31]
Good5.00.51.00.51.01.00.0Andersson
[32]
Good4.50.51.00.01.01.00.0Berger [33]
Good5.00.51.00.51.01.00.0Burton [34]
Good4.50.51.00.01.01.00.0Carlbring [35]
Fair4.00.01.00.01.01.00.0Choi [36]
Fair3.50.01.00.01.00.50.0Clarke [37]
Fair3.00.01.00.00.01.00.0de Graaf [38]
Poor2.50.00.00.01.01.00.0Holländare
[39]
Good5.51.01.00.51.01.00.0Høifødt [40]
Fair4.00.01.00.01.01.00.0Johansson
[41]
Fair3.50.00.00.51.01.00.0Johansson
[42]
Good5.01.01.00.51.00.50.0Kay-Lambkin
[43]
Fair4.00.01.00.51.00.50.0Kessler [44]
Fair4.01.00.00.51.01.00.0Kivi [45]
Fair3.00.00.00.01.01.00.0Lappalainen
[46]
Poor2.00.00.00.00.01.00.0Lappalainen
[47]
Good5.01.01.00.01.01.00.0Ly [48]
Fair4.00.01.00.01.01.00.0Meyer [49]
Fair4.00.01.00.01.01.00.0Meyer [50]
Fair4.00.01.00.01.01.00.0Morgan [51]
Fair4.00.51.00.50.01.00.0Moritz [52]
Fair4.00.01.00.01.01.00.0Perini [53]
Good6.51.00.51.01.01.01.0Phillips [54]
Fair3.50.00.50.01.01.00.0Preschl [55]
Fair3.50.01.00.01.00.50.0Richards [56]
Fair3.50.01.00.01.01.00.0Richards [57]
Fair4.00.01.00.01.01.00.0Ruwaard [58]
Good5.01.00.01.01.01.00.0Sheeber [59]
Fair3.00.01.00.00.01.00.0Spek [60]
Fair4.00.01.00.01.01.00.0Ström [61]
Fair3.50.01.00.01.00.50.0Titov [62]
Good4.50.01.00.51.01.00.0Titov [63]
Good5.51.01.00.51.01.00.0Vernmark [64]
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Quality ratingTotal score654321Study
Fair4.00.01.00.01.01.00.0Wagner [65]
Poor2.50.00.00.01.00.50.0Watts [66]
Fair4.00.01.00.01.01.00.0Agyapong
[30]
Depression
and anxiety
Good5.51.01.00.51.01.00.0Agyapong
[30]
Fair4.00.01.00.01.01.00.0Andersson
[31]
Fair4.00.01.00.01.01.00.0Andersson
[32]
Good4.50.01.00.51.01.00.0Berger [33]
Fair3.00.01.00.00.01.00.0Agyapong
[30]
Fair3.00.00.00.01.01.00.0Andersson
[31]
Fair4.00.01.00.01.01.00.0Andersson
[32]
The quality rating was based on the total score achieved, and
studies were categorized into three groups according to their
quality scores: (1) good (4.5-7 points), (2) fair (3-4 points), and
(3) poor (0-2.5 points). One point was given for every quality
criterion met, 0.5 points for an incomplete description of the
methodology used, and no points if a quality criterion was not
met. As a consequence of only including RCTs in this review,
it was expected that every study was described as “randomized”
and thus attained at least 1 point on the quality rating score.
Achieving a successful blinding in psychotherapy trials is
generally considered to be very challenging, and the methods
of blinding are seldom described appropriately [67]. As a
consequence, we expected that only a minority of studies would
reach a score higher than 5 points and consequently set the
cut-off scores according to our expectations of study qualities.
Data Synthesis
This systematic review included a broad variety of clinical
subpopulations (eg, differences in baseline severity or age) as
well as treatment programs and types of comparators. Therefore,
the feasibility of conducting a meta-analysis required careful
consideration because the calculation of a mean treatment effect
across studies could be irrelevant if studies varied significantly
with regard to study populations, interventions, comparisons,
or methods [68]. The protocol prespecified that if there was an
adequate number of comparable studies, a random-effects
meta-analysis according to the methodology of DerSimonian
and Laird [69] would be conducted for the combined study
groups of depression and depression comorbid with anxiety.
Tables 3 and 4provide a summarizing overview of the included
studies.
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Table 3. Summary of study characteristics, including location, sample size, study name, severity, and age of participants (N=45).
AgeSeverityNameNLocationStudy
Depression
Adults (≥16 years)Moderately severe to severeNo name54IrelandAgyapong [30]
Adults (≥16 years)Mild to severeNo name69SwedenAndersson [31]
Adults (≥16 years)Mild to moderateNo name117SwedenAndersson [32]
Adults (≥16 years)Mild to severeDeprexis76Switzerland GermanyBerger [33]
Adults without older
adults (18-75 years)
Mild to severeHelp4Mood28Romania Spain Scot-
land UK
Burton [34]
Adults (≥16 years)Mild to moderateDepressions-hjälpen80SwedenCarlbring [35]
Adults (≥16 years)Minimal to moderately severeBrighten Your Mood Pro-
gram
63AustraliaChoi [36]
Adolescents (14-24
years)
NRNo name160USAClarke [37]
Adults without older
adults (18-75 years)
Mild to moderateColour Your Life303Netherlandsde Graaf [38]
Adults (≥16 years)MildNo name84SwedenHolländare [39]
Adults without older
adults (18-75 years)
Moderate to severeMoodGYM (Norwegian
Version)
106NorwayHøifødt [40]
Adults (≥ 16 years)Mild to severeSUBGAP92SwedenJohansson [41]
Adults (≥ 16 years)Mild to severeNo name121SwedenJohansson [42]
Adults (≥ 16 years)Mild to severeSHADE97AustraliaKay-Lambkin [43]
Adults without older
adults (18-75 years)
Mild to severeNo name297UKKessler [44]
Adults (≥16 years)Mild to moderateDepressions-hjälpen92SwedenKivi [45]
Adults (≥16 years)Mild to severeGood Life Compass39FinlandLappalainen [46]
Adults (≥16 years)Mild to severeGood Life Compass38FinlandLappalainen [47]
Adults (≥16 years)Mild to severeNo name93SwedenLy [48]
Adults without older
adults (18-75 years)
Moderately severe to severeDeprexis163GermanyMeyer [49]
Adults (≥16 years)NRDeprexis396GermanyMeyer [50]
Adults (≥16 years)Mild to severeMood Memos1736UK Australia Canada
Ireland New Zealand
USA
Morgan [51]
Adults without older
adults (18-75 years)
NRDeprexis210GermanyMoritz [52]
Adults (≥16 years)Mild to severeSadness48AustraliaPerini [53]
Adults (≥16 years)NRMoodGym637UKPhillips [54]
Older adults (≥50
years)
Minimal to moderateE-mental Health Butler
System
40SwitzerlandPreschl [55]
Adults (≥16 years)Mild to moderateSpace from Depression262IrelandRichards [56]
Adolescents (14-24
years)
Mild to moderateBeating the Blues101IrelandRichards [57]
Adults (≥16 years)Minimal to moderateNo name54NetherlandsRuwaard [58]
No age restrictionsNRMom-Net70USASheeber [59]
Older adults (≥50
years)
Subthreshold depressionLewinsohn’s Coping With
Depression Course
301NetherlandsSpek [60]
No age restrictionsMild to moderateNo name48SwedenStröm [61]
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AgeSeverityNameNLocationStudy
Older adults (≥50
years)
Mild to moderateManaging Your Mood
Course
54AustraliaTitov [62]
Adults (≥16 years)Mild to severeSADNESS141AustraliaTitov [63]
Adults (≥16 years)mild to moderateNo name88SwedenVernmark [64]
Adults (≥16)Minimal to severeNo name62SwitzerlandWagner [65]
Adults (≥16 years)Mild to moderateGet Happy (Mobile app of
the sadness program)
52AustraliaWatts [66]
Depression and anxiety
Adults (≥16 years)Moderate to severeNo name57SwedenJohansson [70]
Adults (≥16 years)Minimal to moderateUniWellbeing Course31AustraliaMullin [71]
Adults (≥16 years)Mild to severeWorry and Sadness Pro-
gram
109AustraliaNewby [72]
Adults without older
adults (18-75 years)
NRBeating the Blues167UKProudfoot [73]
Adults without older
adults (18-75 years)
Mild to severeTransdiagnostic Wellbeing
Course (TD-CBT) or Dis-
order-Specific Mood
Course (DS-CBT)
290AustraliaTitov [74]
Adults (≥16 years)Moderate to severeWellbeing Course93AustraliaTitov [75]
Adults (≥16 years)Mild to severeWellbeing Course38AustraliaTitov [76]
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Table 4. Summary of the study treatment and control groups and their relevant scores at baseline and follow-up (N=45).
Follow-upBaseline
Control groupa
Treatment groupa
Study
ControlTreatmentWksControlTreatment
Mean
(SD)
nMean
(SD)
nMean
(SD)
nMean
(SD)
n
Depression
16.60
(9.80)
268.60
(7.90)
241331.99
(9.50)
2831.58
(7.70)
26Thank you text message
+ TAU
Supportive text messages sent
by a computer + TAU
Agyapong
[30]
17.90
(8.80)
3313.60
(10.10)
32925.30
(6.60)
3624.00
(7.70)
33Group CBTGuided Web-based CBTAndersson
[31]
19.50
(8.10)
4912.20
(6.80)
361020.90
(8.50)
4920.50
(6.70)
36Web-based discussion
group only
Web-based CBT+ Web-based
discussion group
Andersson
[32]
28.50
(9.40)
2217.30
(10.20)
251029.80
(8.60)
2628.80
(8.20)
25WLCLow-intensity therapist-guided,
computerized CBT
Berger [33]
17.60
(6.80)
913.90
(8.10)
12421.80
(6.80)
1319.60
(8.10)
14TAUHelp4Mood (Self-report and
biometric monitoring + ele-
ments of CBT) + TAU
Burton [34]
23.43
(7.67)
3816.65
(8.04)
40825.13
(5.19)
4026.32
(5.97)
40WLCWeb-based behavioral activa-
tion and acceptance-based
treatment
Carlbring [35]
21.27
(7.86)
2813.48
(9.28)
23820.83
(7.58)
3025.76
(8.53)
28WLCWeb-based CBTChoi [36]
10.10
(0.70)
589.10
(0.70)
58510.30
(0.80)
7710.00
(0.80)
83TAUWeb-based, pure self-help CBTClarke [37]
22.10
(10.20)
9720.60
(10.40)
95927.90
(7.50)
10328.20
(7.70)
100TAUComputerized CBTde Graaf [38]
13.40
(11.90)
399.30
(12.00)
381017.70
(11.50)
4217.00
(11.50)
42Nonspecific support by
an online therapist (email
contact) + monthly rating
Guided, Web-based CBTHolländare
[39]
of their depressive symp-
toms using the MADRS-
S
18.63
(8.64)
5414.20
(8.15)
52722.27
(6.74)
5421.13
(6.85)
52WLC (TAU)Guided, Web-based CBT
(TAU)
Høifødt [40]
20.22
(7.80)
4611.48
(7.80)
421026.33
(6.70)
4626.54
(5.80)
46Web-based structured
support intervention
(psychoeducation and
Web-based psychodynamic
psychotherapy + online thera-
pist contact
Johansson
[41]
scheduled weekly con-
tacts online)
21.67
(9.50)
3913.78
(9.40)
361026.24
(7.90)
4226.44
(7.60)
36Web-based discussion
group with weekly discus-
sion themes related to
Tailored Web-based CBTJohansson
[42]
depression and the treat-
ment of depression
22.95
(10.46)
2117.09
(12.14)
231332.86
(9.59)
2128.57
(9.89)
23No further treatmentComputerized CBT therapy and
motivational interviewing by a
computer program
Kay-Lambkin
[43]
22.00
(13.50)
9714.50
(11.20)
1131733.50
(9.30)
14832.80
(8.30)
149WLC (TAU)Web-based CBT + TAUKessler [44]
14.46
(9.88)
3513.23
(10.94)
301326.09
(9.39)
4725.50
(7.87)
45TAUWeb-based CBTKivi [45]
17.85
(7.34)
2013.34
(6.75)
18720.65
(6.80)
2022.11
(7.79)
19WLCGuided Web-based acceptance
and commitment therapy with-
out face-to-face contact
Lappalainen
[46]
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Follow-upBaseline
Control groupa
Treatment groupa
Study
ControlTreatmentWksControlTreatment
Mean
(SD)
nMean
(SD)
nMean
(SD)
nMean
(SD)
n
9.17
(5.24)
1810.26
(8.20)
19623.11
(6.38)
1920.79
(9.34)
19Face-to-face Acceptance
and Commitment therapy
(ACT)
Guided Web-based acceptance
and commitment therapy
Lappalainen
[47]
13.43
(11.27)
4615.17
(11.51)
44927.32
(7.89)
4728.96
(8.07)
46Full behavioral activationBlended treatment (4 face-to-
face sessions + a smartphone
application used between ses-
sions)
Ly [48]
13.64
(6.14)
7310.08
(6.37)
60917.20
(3.86)
8516.62
(3.44)
78WLC (TAU)Web-based CBT + TAUMeyer [49]
27.15
(10.01)
5719.87
(11.85)
159927.11
(8.98)
7626.72
(9.86)
320WLC (TAU)Web-based CBT + TAUMeyer [50]
11.50
(6.72)
30110.80
(6.84)
273616.90
(5.76)
87416.40
(5.98)
862Emails containing depres-
sion information
Emails promoting the use of
self-help strategies
Morgan [51]
25.67
(11.65)
9020.51
(12.22)
80830.02
(10.18)
10528.81
(11.11)
105WLCWeb-based CBTMoritz [52]
23.33
(9.29)
1717.30
(9.86)
27827.24
(6.18)
1827.30
(7.30)
27WLCGuided Web-based CBTPerini [53]
10.20
(6.00)
1769.90
(6.10)
164614.60
(5.60)
31814.60
(5.40)
311Attention control (5 web-
sites with general infor-
mation about mental
health)
Computer-based CBTPhillips [54]
15.10
(7.80)
1610.00
(6.30)
20816.50
(5.60)
1619.00
(6.60)
20WLCFace-to-face life-review therapy
including computer supple-
ments
Preschl [55]
20.43
(6.97)
9215.67
(7.68)
96820.84
(4.17)
9220.90
(3.83)
96WLCGuided Web-based CBTRichards [56]
11.52
(4.90)
2512.81
(6.90)
21822.70
(4.70)
3721.72
(5.30)
43Therapist-assisted email
CBT
Unguided Web-based CBTRichards [57]
15.60
(7.60)
189.80
(6.50)
361121.30
(5.30)
1819.70
(5.50)
36WLCGuided Web-based CBTRuwaard [58]
22.50
(11.00)
3513.40
(10.40)
341425.40
(9.00)
3526.20
(9.80)
35WLC (TAU)Guided Web-based CBTSheeber [59]
11.43
(9.41)
5611.97
(8.05)
671017.89
(9.95)
9919.17
(7.21)
102Group face-to-face CBTUnguided Web-based CBTSpek [60]
24.04
(6.86)
2417.88
(11.30)
24928.25
(7.08)
2426.92
(9.30)
24WLCTherapist-guided Web-based
physical activity (guided self-
help program)
Ström [61]
12.68
(5.48)
223.96
(2.48)
23812.04
(5.42)
2511.04
(5.62)
27WLCGuided Web-based CBTTitov [62]
26.15
(10.14)
4015.29
(9.81)
7.59
(4.04)
41826.33
(10.46)
4027.15
(9.96)
41WLCClinician-guided Web-based
CBT
Titov [63]
16.60
(7.90)
2910.30
(5.20)
29821.80
(6.60)
2922.20
(5.30)
30WLCWeb-based CBT, email support-
ed
Vernmark
[64]
12.33
(8.77)
2812.41
(10.03)
25823.41
(7.63)
3022.96
(6.07)
32Face-to-face CBTGuided Web-based CBTWagner [65]
13.68
(2.79)
1512.53
(3.26)
1010830.90
(2.55)
2033.46
(2.95)
15Computer-based CBTSmartphone-based CBTWatts [66]
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Follow-upBaseline
Control groupa
Treatment groupa
Study
ControlTreatmentWksControlTreatment
Mean
(SD)
nMean
(SD)
nMean
(SD)
nMean
(SD)
n
Depression
and anxiety
10.59
(6.40)
295.89
(2.80)
281015.07
(4.40)
2915.32
(3.30)
28WLCWeb-based psychodynamic,
guided self-help treatment
based on affect-phobia therapy
(APT)
Johansson
[70]
13.37
(7.42)
118.33
(4.86)
20614.63
(3.35)
1114.10
(3.62)
20WLCWeb-based CBT (total sample:
N=30); clinical subsample
(PHQ-9 ≥10)
Mullin [71]
21.24
(10.56)
5310.48
(8.30)
431022.41
(9.17)
5421.24
(6.98)
46WLCGuided Web-based CBT (Wor-
ry and Sadness Program)
Newby [72]
18.36
(12.65)
5012.04
(10.45)
47824.08
(9.78)
5325.38
(11.05)
53TAUComputerized CBTProudfoot [73]
8.44
(5.14)
1057.36
(5.04)
112815.23
(3.85)
10515.07
(3.57)
112Self-guided Web-based
CBT
Clinician-guided Web-based
CBT
Titov [74]
10.57
(6.16)
467.58
(4.60)
47814.39
(3.33)
4614.64
(3.34)
47self-guided Web-based
CBT without automated
email
Transdiagnostic self-guided
Web-based CBT with automat-
ed email
Titov [75]
12.15
(4.93)
207.67
(5.97)
181013.35
(6.25)
2014.39
(4.27)
18WLCWeb-based CBT with email and
phone support
Titov [76]
aBDI: Beck Depression Index; CBT: cognitive behavioral therapy; PHQ: Patient Health Questionnaire; TAU: treatment as usual; WLC: waiting-list
control.
Statistical Analyses
Each study was summarized in detail in the predeveloped data
extraction table. The primary outcome of the BDI and PHQ was
assessed as a continuous measure of effect in an additional table.
Because moderate to substantial heterogeneity among the
interventions was expected, mean effect sizes were calculated
using a random-effects meta-analysis according to DerSimonian
and Laird [69]. Review Manager 5 (RevMan 5) was used to
conduct this systematic review [77]. In general, a Pvalue of
<.05 was considered statistically significant.
Calculation of Effect Sizes: Changes in Primary
Outcome Measures Between Pre- and Posttreatment
For every technology-based intervention, to assess the
within-group effect (uncontrolled effect size) of treatments, we
calculated the standard mean difference (SMD) as effect size
referring to the difference between baseline and postintervention,
divided by the pooled standard deviation of each primary
outcome measure, and the 95% confidence intervals around the
effect sizes. According to the methodology described in Hedges
[78], effect sizes were also adjusted to address small sample
sizes. As a consequence of the interdependency of baseline and
posttreatment values, the correlation between time points was
required. However, because the majority of included studies
did not provide the correlation between these values, a
conservative value of .50 was used as suggested by Balk et al
[79].
Calculation of Effect Sizes: Technology-Based
Interventions Versus Control Conditions
We calculated the effect size (SMD or Hedges’ g[78]) for each
comparison between a technology-enabled intervention and a
control condition. It indicates the difference between the two
study groups at posttest (standardized mean difference) and the
95% confidence intervals around the effect sizes. Effect sizes
resulted from the subtraction of the mean score of the
intervention group at posttreatment from the mean score of the
comparator group and dividing the result by the pooled standard
deviation of the two groups. Values of 0.8 refer to large, 0.5 to
moderate, and 0.2 to small effects [80]. To address small sample
sizes, we also adjusted effect sizes according to the procedures
described by Hedges [78]. Only those instruments that measured
depressive symptoms were used in the calculation of effect
sizes.
In cases in which more than one depression measure was
provided, the BDI was preferred over the PHQ. If studies only
assessed the PHQ score, the PHQ was used for calculations. In
this analysis, only the effect sizes referring to the differences
between the two study groups at posttreatment were used.
Because the follow-up period varied considerably between
studies, we decided not to examine the differential effects at
these time points.
Assessment of Heterogeneity
As a consequence of the anticipated moderate-to-high level of
diversity between study populations and interventions eligible
for this systematic review, the Breslow-Day test was used to
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test for heterogeneity [81]. To complement the common
chi-square test for heterogeneity, the I2statistic proposed by
Higgins et al [82] was used. Inconsistency (termed I2) was
calculated by the formula: I2=max 0, 100%*(Q-df)/Q, where Q
is the heterogeneity statistic and df its degrees of freedom.
Because I2is not inherently dependent on the number of studies,
this characteristic is of advantage in assessing the percentage
of total variation across studies due to heterogeneity. An I2value
greater than 50% was considered as strong inconsistency [83].
Subgroup and Sensitivity Analyses
Because a high degree of heterogeneity was to be expected, we
tried to mitigate this issue by subgroup analyses. We tested
prespecified hypotheses to assess the robustness of the findings
and to explore sources of heterogeneity (relationships between
study characteristics and intervention effects). The following
hypotheses were proposed. It was assumed that the treatment
effect was influenced by (1) duration of treatment, (2) severity
of depression (baseline score), (3) age of participants, (4)
methodological quality of studies, (5) type of control (eg, TAU,
WLC), (6) inclusion of face-to-face therapist sessions, and (7)
utilization of CBT techniques.
Assessment of Publication Bias
The data collection was based on the description of interventions
in published literature. Thus, grey literature assessing the
effectiveness of technology-based interventions was not taken
into account. The potential presence of publication bias likely
had a significant impact on the results of this study, not only
with respect to differences in usage of online interventions in
clinical settings, but also in more real-world settings [84]. To
improve and standardize the description of technology-based
interventions, it is suggested that future studies apply
frameworks such as the Consolidated Standards of Reporting
Trials (CONSORT) statement for eHealth [85], a protocol for
systematic reviews [86] and guidelines for reporting online
intervention research [73]. Language bias might be an issue in
this review because only RCTs published in the English
language were included. These limiting factors should be kept
in mind when interpreting the findings of the current work. In
order to identify cases of possible publication bias, a funnel plot
was drawn for the main analysis [87]. Nonpublication of small
trials would result in asymmetry of the plot. In addition, the
funnel plot was evaluated for asymmetrical distributions. To
confirm the visual interpretation, which can be subjective, the
Begg and Mazumdar [88] adjusted rank correlation test for
publication bias was used.
Results
In this section, the findings of the different analyses that were
carried out in this review are reported. Characteristics of studies
are presented in tabular form.
Study Selection
The searches in MEDLINE, PsycInfo, and the Cochrane
Controlled Trials Register identified a total of 6387 citations
(articles and abstracts) published after 2000. After the
adjustment for duplicates and the exclusion of noneligible trials
based on titles and abstracts, 491 studies remained. Forty-two
additional possibly eligible trials were identified by checking
the reference lists of relevant articles already identified. A more
detailed review of the full text of the remaining citations led to
the detection and exclusion of 130 publications. Thirty-four
trials were excluded because of the lack of appropriate reporting
of outcomes. It was decided by consensus to exclude two
additional studies that included an active control group that only
differed from the study group in the use of a program component
that was not relevant to this review. In total, 45 RCTs were
included. Of these, seven trials analyzed patients with depressive
symptoms comorbid with anxiety. Figure 2 summarizes the
study selection process.
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Figure 2. Flow diagram of study selection.
Characteristics of Studies and Risk of Bias Within
Studies
Tables 3 and 4provide information on the setting and outcomes
of the 45 trials finally included in the analysis. These RCTs
contributed a total of 7326 randomized and 4519 analyzable
patients. The majority of trials studied CBT in adult patients
with mild-to-moderate depressive symptoms.
The risk of bias at the level of the individual trials is addressed
in Table 2 by reporting the modified Jadad score [19] and
whether analysis was performed according to the
intention-to-treat principle (see Assessment of Methodological
Quality). None of the trials were described as “double-blind
RCT” and we also noted a relatively high risk of bias due to the
insufficient blinding of participants. Typically, the method of
blinding was described insufficiently. This, however, is not
uncommon for psychotherapy trials [67]. In general, achieving
a successful blinding in psychotherapy trials is regarded to be
more demanding than in pharmacological trials [89]. Study
participants can easily identify the discrepancies in the contents
of the treatment and control arms, and it is probably not very
likely to successfully blind the participants or the therapists.
Thus, the methods used to achieve proper blinding are rarely
reported in psychotherapy trials [67]. The risk of bias introduced
by selective reporting was small because all outcomes of interest
were adequately described in the vast majority of the included
RCTs.
System Components
In total, a set of 15 system components was identified based on
occurrence in reviewed literature. These were either defined,
hypotheses-driven, or derived from Oinas-Kukkonnen’s model
of persuasive systems design [18]. For each study included in
the systematic review, we determined the presence of defined
system components for later analysis. In the following, we
present an overview of identified components, together with
the underlying inclusion rationale.
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Channel of Delivery
Technology-mediated MHIS can be administered using a range
of available technologies. Although early interventions were
based on offline programs, computerized programs and
Internet-delivered Web interventions have become more popular
in recent years [16]. Following the latest development, the
mobile phone as a channel of delivery is getting more and more
attention. This seems reasonable because almost half of the
world’s population has a mobile phone subscription, and it is
expected that by 2020 the global penetration rate will reach
approximately 60% [90]. In addition, it is suggested that
differences in access to mobile technologies are diminishing at
least for nonrural populations, thus offering an opportunity to
reach underserved and marginalized populations [91]. The high
global penetration and the rapid growth of mobile phone apps
provide the opportunity to reach an increasing number of people
who are in need of treatment for mental disorders [92].
Tailoring (Personalization)
Oinas-Kukkonen argued that “information provided by the
system will be more persuasive if it is tailored to the potential
needs, interests, personality, usage context, or other factors
relevant to a user group” and that “a system that offers
personalized content or services has a greater capability for
persuasion” [18]. This was confirmed in the context of health
behavior change by a systematic review [93] and presents a
promising component for the treatment of depression in MHIS.
Supportive Text Messages (Tunneling/Praise)
Research by Agyapong et al [30] targeting the support for people
with depression and comorbid alcohol use disorder presented
promising results in the deployment of supportive text messages.
This followed the Oinas-Kukkonen concept of tunneling [18]
(ie, “using the system to guide users through a process or
experience provides opportunities to persuade along the way”)
and the concept of praise, which is said to make users more
open to persuasion [17].
Peer Support
Although no consensus with respect to effectiveness of online
peer support was reached yet [94,95], anonymous online support
groups and discussion forums might help users to overcome the
feeling of being stigmatized by connecting patients with others.
A further advantage of these social support components is that
time and location are no longer obstacles for active participation
[96]. Peer support follows the Oinas-Kukkonens [18] concept
of social learning, social comparison, and social facilitation
[96].
Case-Enhanced Learning
This form of learning uses educational stories that identify a
problem and a solution with an example (ie, a case) the
participant can potentially identify with [75]. These can be
implemented via, for example, video vignettes of case-study
patients.
Reminders
Stemming from Oinas-Kikkonens concept of reminders, Whitton
et al [11] found that reminders play a decisive role in the
engagement of users in mental health interventions and are a
cost-effective approach for engaging users [11-13]. Furthermore,
it is suggested that reminders not only enhance user engagement
but also improve adherence [97-99] and counteract the high
rates of nonusage attrition common to many online-based
interventions [97,100].
Downloadable Material
Because a participant’s preferred medium for reading might be
paper [101], an option to download and print out summaries,
lessons, or homework might influence treatment efficacy by
providing a higher level of comfort.
Workbook/Homework Assignments
Homework assignments, as commonly used in standard care
[102], are an important construct in CBT. In a recent study,
LeBeau et al [103] concluded that “improvement of homework
compliance has the potential to be a highly practical and
effective way to improve clinical outcomes in CBT.” Therefore,
implemented in an appealing interactive way, this might
represent an important component in MHIS.
Symptom Tracking
Tracking symptoms, either objectively using sensors [104] or
by means of self-reports might be beneficial for the user
following Oinas-Kikkonens self-monitoring concept [18]. It
describes a “system that helps track one’s own performance or
status supports in achieving goals” [18].
Online Diary
Diaries form a way of self-monitoring and self-reflection and
are frequently used in classical forms of CBT [105].
Summaries
We assessed whether included studies made use of summaries
of content (eg, module or progress summaries), which represent
another dimension drawing on the concept of self-monitoring
and self-reflection [17].
Audio/Voiceover
A recent experimental study found that audience feedback is a
valuable tool to enhance users’ perceptions of health-related
YouTube clips [106], which highlights the power of the
participatory nature of the Web to increase the efficacy of
Internet-based health interventions.
Illustrative Content/Video
Illustrative content in the form of graphics, photos, illustrations,
comics, or video clips might increase the appeal of interactivity
and visual attractiveness of Internet-based programs [107].
Gamification
The use of game-like strategies has demonstrated to produce
positive outcomes in previous studies of technology-based health
interventions [108,109]. Gamification of health apps might
provide the option to set up goals and rules for personal health
behavior and to track patient’s actual behavior against these
rules and goals [108]. The utilization of rewards in the context
of health intervention might be promising because it was found
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that rewards are able to stimulate positive thinking in users and
are thus a powerful tool to drive long-term participation [110].
Animations/Virtual Assistant
Virtual agents or avatars could be used for persuasive purposes
and to support self-management among patients [111]. A
growing body of literature examines the relationship of virtual
agents and their user, potentially holding vast opportunities for
persuasive system design [112-114].
System Component Setting
Table 5 contains detailed information on the settings and system
component configuration of the interventions.
Approximately 45% (20/45) of all included studies reported
using email reminders and 10 of 45 studies (22%) reported
providing SMS text message reminders, mostly as an alternative
to reminders sent by email. In the included trials, reminders
were typically intended to increase motivation and adherence
to therapeutic interventions. As explained earlier, reminders
play a decisive role in the engagement of users in mental health
interventions. Most of the RCTs made use of the Internet for
delivering mental health interventions for depression or
depression comorbid with anxiety. The majority of RCTs
included in this systematic review (91%, 41/45) did not make
use of mobile phones or tablets. Typically, the interventions
required interaction with the system, and many also included
interaction with a therapist (face-to-face or online) and/or peers
on the Web. In all, 80% (36/45) of included programs were
based on CBT or used elements of CBT. Tunneling, which refers
to the stepwise delivery of content, is typically found in
technology-based interventions for depression [18], and was
also used in the majority of studies (87%, 39/45) included in
this work. Twenty-seven of 45 included studies used tailored
content, tailored feedback, and/or tailored reminders.
Only a small number of included studies made use of
self-monitoring components, such as symptom tracking and
tracking reminders, yet they are seen as key features of
psychotherapy in particular [40,96], and in behavior-change
interventions in general [12,115-118].
Although social support is widely recognized as an important
feature in behavior change [119,120], in this analysis, only seven
studies (16%) used peer support. In the included studies, social
support consisted of the use of Web-based discussion boards
(asynchronous social support), which was intended to provide
the ability to connect with other patients of the same
intervention.
Regarding visual attractiveness, 44% of all included studies
analyzed an intervention that included visually appealing
content, such as graphs, illustrations, comics, or photos, which
often serve a motivational purpose. Only 11 of 45 studies (24%)
described the utilization of audio and/or voiceovers. Video
footage, often containing case-enhanced learning, was also
found in 11 RCTs (24%).
In addition, this analysis showed that game elements were only
used in three RCTs and, if gaming was included, it was in the
form of knowledge quizzes. See Figure 3 for a quantitative
overview of components among included studies.
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Table 5. MHIS system component configuration of included studies for health information system (HIS) channel and tailoring and (1) email/phone
reminders, (2) supportive text messages, (3) peer support, (4) summaries of progress or content, (5) case-enhanced learning, (6) material to download/print,
(7) homework assignments, (8) mood rating / symptom tracking, (9) online diary/journal, (10) audio/voiceovers, (11) illustrative content, (12)
games/quizzes, and (13) animations (virtual agent).a
13121110987654321TailoringHIS channelStudy
Depression
00000010000110/NRMobile phoneAgyapong [30]
00000010000000/NROnlineAndersson [31]
0100001100101PartlyOnlineAndersson [32]
00100011000011OnlineBerger [33]
10010100010001OnlineBurton [34]
10110010000001EmailCarlbring [35]
00100010110010/NROnlineChoi [36]
00101110001011OnlineClarke [37]
00101110000100/NROnlinede Graaf [38]
00100110000010/NROnlineHolländare [39]
0010001000001Feedback/re-
minders
OnlineHøifødt [40]
0000001000011Feedback/re-
minders
OnlineJohansson [41]
00000011001111OnlineJohansson [42]
00110010000000/NRComputer programKay-Lambkin [43]
00000000000101OnlineKessler [44]
0011101100000Feedback/re-
minders
OnlineKivi [45]
0011101000001Feedback/re-
minders
OnlineLappalainen [46]
00010110000011OnlineLappalainen [47]
00100010100111Mobile phoneLy [48]
00110111000111OnlineMeyer [49]
10100010100001OnlineMeyer [50]
00000000000110/NREmailMorgan [51]
10100010100001OnlineMoritz [52]
00100011111000/NROnlinePerini [53]
0010001000001Feedback/re-
minders
OnlinePhillips [54]
00110011000001Computer programPreschl [55]
01100010110011OnlineRichards [56]
1011101000000Feedback /re-
minders
OnlineRichards [57]
00000110000101OnlineRuwaard [58]
0001011000100Feedback/re-
minders
OnlineSheeber [59]
00100010000000/NROnlineSpek [60]
00000010000101OnlineStröm [61]
00000010110010/NROnlineTitov [62]
00100011111010/NROnlineTitov [63]
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13121110987654321TailoringHIS channelStudy
01000011000011OnlineVernmark [64]
0000001000000Feedback/re-
minders
OnlineWagner [65]
00100010100000/NRMobile phone vs
PC
Watts [66]
00000010000010/NROnlineAgyapong [30]
Depression and anxiety
0010001000000Feedback/re-
minders
OnlineJohansson [70]
00100010110010/NROnlineMullin [71]
00000011110010/NROnlineNewby [72]
101100110100NR1Computer programProudfoot [73]
00100010110110/NROnlineTitov [74]
00100010100010/NROnlineTitov [75]
00100010111110/NROnlineTitov [76]
aRating system is 1=component present and 0=component not present. CG: control group; NR: not reported; TG: treatment group
Figure 3. System component distribution of included studies (N=45).
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Synthesis of Results: Impact on Symptoms of
Depression
Data from 45 trials (4519 patients) that reported on BDI or PHQ
scores before and after the treatment were combined to estimate
the overall effect of technology-based interventions on
depressive symptomatology. Technology-supported treatments
for depression showed a trend toward reduced depressive
symptoms (SMD=–0.58, 95% CI –0.71 to –0.45; P<.001)
(Figure 4). The chi-square test for heterogeneity (χ2
44=183.3),
across all patient types, was significant (P<.001) and total
variation across trials due to heterogeneity (I2) was 76%, a strong
indication for inconsistency across included RCTs.
Figure 4. Effect of technology-based interventions on symptoms of depression in included studies (N=45).
Risk of Bias Across Studies
The funnel plot drawn for the main analysis on the effectiveness
of technology-delivered interventions for depression showed
an asymmetry, which is evidence of missing studies suggesting
publication bias (Figure 5). Additional testing utilizing the Begg
and Mazumdar rank correlation method [88] confirmed that
there is, in fact, evidence of publication bias (P=.03).
As a consequence of this finding, we also explored publication
bias in the 22 RCTs that used a WLC group. The funnel plot of
this subgroup of included trials showed no evidence of bias,
which was confirmed by the Begg test (P=.08).
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Figure 5. Risk of bias across all included studies (N=45).
Results of Subgroup Analyses
Subgroup analyses by study quality, treatment duration,
provision of face-to-face contact, utilization of CBT techniques,
severity of baseline depression, age of participants, and type of
control (eg, TAU or WLC) were prespecified and served to test
our hypotheses (see Subgroup and Sensitivity Analyses ). The
individual forest plots per subgroup analysis can be found in
Multimedia Appendixes 1-7.
Study Quality
Differences in study findings could also be explained by biased
results due to differences in quality of individual studies. Thus,
a subgroup analysis based on the methodological quality of
included trials was performed. It could be shown that the effect
of technology-based interventions on symptoms of depression
is consistent in trials of higher (quality score >3.5) and lower
quality (quality score ≤3.5). In high-quality trials,
technology-based interventions were associated with an SMD
of –0.60 (95% CI –0.76 to –0.45, P<.001; I2=77%) and a SMD
of –0.53 (95% CI –0.77 to –0.29, P<.001; I2=76%) in
low-quality studies. It seems that methodological quality of
trials only showed a small impact on effect sizes, but overall
had a moderate effect (χ2
1=0.3, P=.60; I2=0%).
Treatment Duration
As statistical heterogeneity was found and to exclude the
possibility of heterogeneity due to differences in the duration
of the interventions, trials of different durations were compared
to one another. Regarding depressive symptomatology,
technology-based interventions showed to be effective,
irrespective of treatment duration. In treatments with a duration
of 10 weeks or less, the intervention was associated with an
SMD of –0.60 (95% CI –0.76 to –0.44, P<.001; I2=79%).
Similarly, treatments with a duration of longer than 10 weeks
resulted in an SMD of –0.52 (95% CI –0.70 to –0.33, P<.001;
I2=48%). There was no evidence for an association between
duration of treatment and the effect of treatments on depressive
symptomatology (χ2
1=0.4, P=.50; I2=0%).
Provision of Face-to-Face Contact
As statistical heterogeneity was found in our analysis of
treatment effectiveness and in order to exclude the possibility
of heterogeneity due to the provision of face-to-face contact,
trials that incorporated face-to-face support were compared to
interventions that did not use live contact with a therapist. We
hypothesized interventions that included face-to-face sessions
would show greater reductions in depressive symptoms than
treatments that did not. Contrary to what was expected, effect
size in treatments that did not use face-to-face support was larger
(SMD –0.65, 95% CI –0.79 to –0.50, P<.001; I2=78%) than in
interventions that offered live support (SMD –0.28, 95% CI
–0.52 to –0.03, P=.03; I2=50%). An association between the
effectiveness of treatments and the provision of face-to-face
contact was found (χ2
1=6.34, P=.01; I2=84%).
Utilization of Cognitive Behavioral Therapy
We made the hypothesis that technology-based interventions
using CBT techniques are more effective than treatments that
do not use components of CBT, reflecting on the predominance
of CBT in the literature. Effect size varied only slightly between
trials that were based on CBT (SMD –0.58, 95%CI –0.72 to
–0.45, P<.001; I2=71%) and interventions that did not use a
CBT protocol (SMD –0.56, 95% CI –0.96 to –0.16, P=.006;
I2=85%). Also, according to the test for subgroup differences,
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there was no association between the effect of
technology-supported treatments on depressive symptoms and
utilization of CBT methods (χ2
1=0, P=.90; I2=0%).
Severity of Baseline Depression
Comparing trials in which patients showed higher depression
scores (BDI ≥25 or PHQ ≥15) with trials in which patients had
lower scores (BDI <25 or PHQ <15), technology-based
treatment showed to be effective in both groups (higher severity:
SMD –0.61, 95% CI –0.79 to –0.44, P<.001; I2=77%; lower
severity: SMD –0.54, 95% CI 0.75 to –0.33, P<.001; I2=75%).
The test for subgroup differences resulted in a chi-square value
of 0.3 (df=1, P=.59; I2=0%).
Age of Participants
Age did not show a significant impact on effectiveness of
technology-based interventions for the treatment of depression.
Although effect sizes varied between adults (SMD –0.63, 95%
CI –0.79 to –0.46, P<.001; I2=79%), adults excluding older
adults (SMD–0.48, 95% CI –0.69 to –0.27, P<.001; I2=55%),
older adults (SMD –0.53, 95% CI –1.52 to 0.46, P=.29;
I2=84%), and adolescents (SMD –0.28, 95% CI –1.45 to 0.88,
P=.63; I2=91%), subgroup analyses pointed in the same direction
(see Table 1 for a description of the different age categories).
The effect of technology-based interventions on symptoms of
depression was not associated with patient age (χ2
3=1.4, P=.71;
I2=0%).
Type of Control Condition
Comparing technology-based treatments to TAU resulted in a
moderate effect (SMD –0.48, 95% CI –0.78 to –0.18, P=.002;
I2=62%) and the comparison of technology-supported
interventions for depression to WLC showed a large effect
(SMD –0.79, 95% CI –0.93 to –0.64, P<.001; I2=51%). The
test for subgroup differences resulted in a chi-square value of
3.3 (df=1, P=.07; I2=69.3%).
Discussion
This systematic literature review had the following objectives:
(1) to collect all relevant clinical studies of technology-based
interventions that analyzed the effectiveness for the treatment
of depression in order to accurately depict the body of literature
and (2) to identify a set of system components of technology-
and Internet-based interventions for depression. In general, the
results are in line with previous analyses and showed that
technology-supported interventions, in fact, reduce depressive
symptoms [5,6,121]. This study is one of the first that provides
an overview of technical components used in the current set of
RCT trials that made use of computerized and online
interventions on the treatment of depression.
Principal Results
Forty-five publications with a total number of 7326 randomized
and 4519 analyzable participants were included in this
systematic review, and most of the interventions analyzed were
able to reduce symptoms of depression. The majority of included
studies were of fair (60%) to good (33%) quality, and almost
every study included analyzed an intervention that was modular
in setup and typically lasted for approximately 10 weeks.
Thirty-six studies (80%) deployed a CBT approach. This extends
the systematic review by Saddichha et al [8], which consisted
of 29 RCT studies using CBT. Usually, the programs were
aimed to be used about once a week. This is in line with
traditional CBT, which is seen as a step-by-step, short-term
treatment with weekly or biweekly therapist sessions for 10 to
20 weeks [17].
Subgroup analysis showed that study quality, treatment duration,
provision of face-to-face contact, utilization of CBT, and age
of participants had relatively small impact on the outcome of
the interventions. For study quality, it is plausible because the
intervention quality is not inevitably reflected by the study
quality. Likewise, there are reasonable explanations that the age
of the participants and treatment durations did not have a
decisive impact on treatment outcome. We probably
overestimated the influence of technology literacy in older
participants because our results confirm findings of literature
showing comparable treatment results for all age groups. The
small difference in effect for treatment durations can also be
explained taking into account that traditional therapy has a
similar range of time spans to deliver the same amount of
structured information and is chosen depending on, for example,
severity of illness and level of support [122]. We were surprised
that provision of face-to-face therapy did not show significant
effects on the treatment outcome. In fact, this confirmed a recent
study comparing traditional therapy with a computerized
intervention [65].
Interestingly, the use of CBT components also did not show
significant effects on treatment outcome compared to
interventions that were not based on CBT. Although there exists
evidence that other types of intervention can be equally effective,
predominance of CBT in traditional therapy as well as in
technology-mediated MHIS urged us to test its superiority in
our analysis.
As explained earlier, reminders play a decisive role in the
engagement of users in mental health interventions. In this
systematic review, approximately 45% of all included studies
reported to use email reminders and 10 studies (22%) reported
providing phone reminders, mostly as an alternative to reminders
sent by email. In the included trials, reminders were typically
intended to increase motivation and adherence to therapeutic
interventions.
With respect to the design and effective use of reminders,
research indicates that a variety of factors show an impact on
the efficiency of these alerts. Firstly, it was found that there is
a high risk that motivational emails provided within a workplace
setting are easily ignored as a consequence of a full email inbox
[123,124]. Thus, it might be of importance to offer the
possibility to choose between mobile phone or email reminders
and to customize time points at which users will be reminded
to complete program modules. Secondly, regarding the content
of expert-initiated contact, it is postulated that contacts
delivering behavior-change techniques might be of greater
effectiveness than simple messages that prompt users to access
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the intervention [10]. Thirdly, it is suggested that reminders
containing short motivational messages, quotes, or facts might
counteract negative feedback cycles that maintain perceptions
of low self-worth and associated depressive symptomatology
[11,125]. Lastly, it could also be shown that the personalization
of reminders and sending them out frequently enhances the
effectiveness of treatment [98]. Nevertheless, nonspecific
factors, such as encouragement, empathy, and hopefulness of
improvement, may also independently enhance therapeutic
gains. To conclude, it seems evident that creating a sense of
being continuously supported and encouraged is crucial for user
engagement, treatment adherence, and buffering against the
development of negative feedback cycles. The regular and
consistent receipt of well-designed reminders, motivational
messages, and tips may thus be a very powerful means of
reminding patients that they are actively working on gaining
control over their symptoms [11].
Most of the RCTs included made use of the Internet for
delivering mental health interventions for depression or
depression comorbid with anxiety. The majority of RCTs
included in this systematic review (93%, 42/45) did not make
use of mobile phones or tablets, which is surprising because the
benefits with respect to user engagement and adherence seem
apparent. Typically, the interventions required the interaction
with the system and many also included the interaction with a
therapist (face-to-face or online) and/or peers on the Web. In
all, 80% (36/45) of included programs were based on CBT or
used elements of CBT. Given the fact that therapeutic
interventions for depression are commonly based on CBT
techniques and psychoeducation, which follow a stepwise
approach and are usually delivered in person by a therapist,
these findings support the authors’ premise. Twenty-seven of
45 included studies used tailored content, tailored feedback,
and/or tailored reminders. In the opinion of researchers, the
adaptation of information to factors that are relevant to one
individual or a group of individuals is an important feature in
effective health communication [93,126,127]. In fact, van
Genugten et al [128] found that interventions that are more
flexible in use (easy to handle for both advanced as well as
novice users), provide structure (which is comprehensible to
the user and that the user knows at what point he or she is in
the process), and use default settings are more likely to be
effective. With respect to treatment exposure, it is suggested
that personally tailored feedback and goal setting are among
the important factors related to the use and exposure to
Web-based interventions. In the study by Brouwer et al [129],
exposure was regarded as the time spent on the website, page
views, and the number of times the user logged on. Although
there is a relationship between exposure, adherence, and
treatment outcomes, focusing on exposure only gives a limited
insight into the pattern of usage and adherence [97].
Doherty et al [130] showed that user engagement also depends
on depression severity and that users with minimal symptoms
engage much less than other groups. To serve the needs of this
patient group, users might benefit from more flexibility (eg, in
the elements of the intervention they wish to focus on).
Therefore, new approaches such as tailoring of interventions
that are more lightweight are needed. However, due to the
severity of the disorder, patients with more pronounced
symptoms face specific difficulties in engaging with the
program. Because these patients require more intensive support
than patients with less severe symptoms, they should also be
given the choice to change the means of support throughout the
treatment. Thus, mechanisms are needed that allow for
requesting or being offered face-to-face contact even if the user
has initially commenced online treatment. We assume that
customization of programs is necessary to enhance long-term
adherence and outcomes of interventions.
Only a small number of included studies made use of
self-monitoring components, such as symptom tracking and
tracking reminders, yet they are seen as key features of
psychotherapy [11,96] and in behavior-change interventions in
general [99,115-118].
Although social support is widely recognized as an important
feature in behavior change [119,120], in this analysis only seven
studies (16%) used peer support. In the included studies, social
support consisted of the use of Web-based discussion boards
(asynchronous social support), which intended to provide the
ability to connect with other patients using the same intervention.
However, the literature shows that there is disagreement with
regard to use and benefit of discussion forums and chat rooms.
Although many studies support the use of these components
[123,131,132], others do not [133-135]. The effectiveness of
peer support depends on individual factors, such as perception
of the credibility of Internet-based peer advice and perceived
quality of interaction [10]. Furthermore, effectiveness might
also rely on user involvement. It could be shown that users that
actively post and respond to messages are more likely to benefit
than users that participate only passively [132].
Regarding visual attractiveness, 44% of all included studies
analyzed an intervention that included visually appealing
content, such as graphs, illustrations, comics, or photos, which
often serve a motivational purpose. Only 11 studies (24%)
described the utilization of audio and/or voiceovers. Video
footage, often containing case-enhanced learning, was also
found in 11 RCTs (24%).
In addition, this analysis showed that game elements were only
used in three RCTs and if gaming was included, it was in the
form of knowledge quizzes. Relatively few studies have
incorporated games as part of their persuasive design.
Although virtual reality has shown to be effective in the
treatment of anxiety and pediatric disorders [136], so far there
is no study utilizing this technology for the treatment of
depression and this might open a promising direction [137].
With respect to virtual agents and synthesized speech, Morrison
et al [10] were also not able to show an association between
digitized speech and improved outcomes in depression. To date,
technological advances and improved design of animations and
avatar-based systems are likely to permit the development of
sufficiently sophisticated services to simulate real interaction
[10] and, therefore, might show more impact on treatment
outcomes. Future research is needed that concerns the
effectiveness of serious gaming and virtual reality in
technology-supported mental health interventions currently
underrepresented in literature.
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Limitations and Future Directions
The list of factors that influence user friendliness as well as the
different platforms for delivery included in this analysis is not
exhaustive. In fact, the majority of RCTs included in this
systematic review did not make use of mobile phones or tablets.
It is expected that especially newer studies could use different
channels of service delivery (eg, mobile phone or tablet
delivery). Consequently, studying future interventions that make
use of these delivery channels would give an interesting insight
into the influence of different modes of delivery on treatment
effectiveness.
Further limitations are related to the strict process of study
selection applied in this systematic review. Many trials were
excluded because (1) they were not described as being
randomized, (2) participants showed no symptoms of depression
at baseline, (3) they included other mental health disorders, and
(4) they did not assess one of the outcomes of interest. In fact,
the decision to only include RCTs might lead to potential
limitations of this systematic review. Even though RCTs are
regarded as the “gold standard” of reliable evidence, the criteria
to only include RCTs might lead to the exclusion of relevant
articles that examined the effectiveness of MHIS, but used a
different study design. Primarily, the exclusion of non-RCTs
in this review lead to a facilitated analysis of studies because
differences in methodological quality are, although not
completely removed, limited. A possible consequence of this
decision might be that we missed studies of newer interventions,
which might not yet be evaluated in an RCT study format
because they are undergoing their piloting phase at the current
time [15,104].
In addition, as a consequence of considering a wide range of
MHIS, included trials differed considerably in the type of
therapeutic programs they used, baseline depression severity,
age of participants, duration of treatment, type of control
condition, methodological quality, and the various system
components they utilized to enhance user engagement,
motivation, and effectiveness of the intervention. As a
consequence of this moderate-to-high level of heterogeneity
across included trials, comparability is restricted and results
should be handled with care. Subgroup analyses demonstrated
that there is, in fact, a significant association between the
effectiveness of interventions and the provision of face-to-face
contact as well as the type of control they used. As previously
noted, the inclusion criteria stated in the protocol also included
other mental disorders such as anxiety disorders. However, the
vast number of articles identified in the electronic search posed
an additional challenge. Consequently, it was decided to focus
on depression and depression comorbid with anxiety only.
Considering that many mental health problems often co-occur
[138], study findings might be constrained as a result of this
relatively strict inclusion/exclusion of certain mental disorders.
Because technology-based psychological interventions adapt
established methods of treatment and only the means of delivery
are altered, the issue of noninferiority plays a major role in this
review. With respect to the overall effectiveness of
technology-based interventions, it is of utmost importance to
review the literature from the perspective of noninferiority trials
that compare an established evidence-based treatment (eg, CBT)
with a new one (eg, technology-based CBT). It also needs to
be clarified that the absence of a significant difference between
two interventions in an RCT cannot be equated with
noninferiority and that the comparison of treatment effects
between studies are only appropriate if the new and existing
treatments are compared against a reference that does not
substantially differ in methods and population [2].
In addition, identifying the points of disengagement and gaining
deeper insight into the patterns of program usage is crucial for
the refinement of system components that are most strongly
associated with user engagement and symptom improvement.
Data on patterns of use further offer an opportunity to refine
content, means of delivery and to adapt both to the needs and
preferences of specific groups of users [130]. Additional
research is needed to overcome these shortcomings by assessing
the association of patterns of component use and the
improvement of symptoms by means of advanced statistical
analyses. Furthermore, it is suggested that RCTs assess symptom
reduction more frequently to obtain improved information about
nonlinear relationships between patterns of usage and therapeutic
gains. To enhance the understanding of when and how to choose
and use different system components, it also is important to
comprehend the dose-effect relationship of different components
[128]. Future studies should be aimed to clarify the causal
relationship between patterns of program usage and symptom
improvement by assigning participants to different system
components. In addition, it is likely that other predictors such
as age, gender, and education affect the relationship between
usage of system components, engagement, and outcomes.
Therefore, additional studies are needed to assess whether
differences exist between those populations.
In regard to designing mental health interventions, it is of utmost
importance to understand what users need and expect from
computerized or Web-based mental health interventions and
how individuals rate different system components with respect
to usefulness, practicality, connectivity, time demands,
professional support, social interaction, convenience, novelty,
reliability, confidentiality, trustworthiness, motivation, and
engagement. Qualitative feedback offers a solution to find
answers to the proposed questions. Likewise, user feedback
might disclose disparities between user expectations and actual
results. It can be assumed that user preferences vary greatly
from individual to individual. This, in turn, supports the rationale
of customization and tailoring of programs to create unique user
experiences based on client’s preferences without losing the
effectiveness of interventions.
Moreover, to clarify the clinical feasibility of computer- and
Web-supported mental health interventions, it also is important
and worthwhile to repeatedly listen to the opinion of therapists.
Apart from the time and cost savings, there is a need for a
thorough understanding of the program, which could be achieved
by the provision of a protocol, printed manual, and an overview
of the program to the therapists involved. In addition, more
detailed information on their practices and how to deal with
clients who are not engaging with the program should be
provided [130].
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Although clinicians tend to be very self-protective about their
time commitments and skeptical about technology [139], value
in health care should always be defined around the customer.
Nonetheless, because value in health care is measured by the
outcomes achieved, value depends on results not input [140].
First, computer-supported interventions should be designed and
modified to optimize clients’ benefits. Although value in health
care is measured by the outcomes achieved, only assessing the
effect on symptom reduction fails to include the value of
mediators that might explain behavior change (eg, factors such
as skills, attitudes, and self-efficacy) [128]. Thus, a further
shortcoming of the current meta-analysis is the lack of analyses
of potential mediators of the effect.
Implications and Recommendations
From a system component perspective, there is a strong need
to counteract the decrease of program usage over the course of
the intervention that is typically found in unguided
technology-mediated interventions for mental health
[118,141,142]. Thus, it is of great importance to implement the
program components that are associated with improved and
regular program engagement. One of the factors that have been
linked to a decrease in module completion rate is obliging users
to complete module sessions in a predetermined sequence [143].
On the one hand, the delivery of module sessions in a tunneled
format bears many advantages, such as a greater number of
website pages accessed, greater time spent on the website, and
greater knowledge gained from the website [143]. On the other
hand, tunneling might lead to a decrease in module completion
because users are required to complete the between-session
homework assignments before being able to start the next
module session [11]. Therefore, developers have to enable a
high level of flexibility in the choice of relevant modules as
well as the speed with which users proceed through the modules
to keep users engaged. One approach to allow for a greater
flexibility, if not violating the underlying psychological theory,
is to make homework assignments optional [11] or to leave it
to the user at what stage they prefer to complete the homework
tasks. Future studies are needed to confirm whether the provision
of more user-driven programs might offer an advantage over
the frequently used linear delivery of interventions in mental
health interventions [139].
In addition, little is known about the synergistic effects of
behavior-change components, modes of delivery, and user
friendliness. Van Genugten et al [128] found synergistic effects
in interventions that made use of a specific CBT components
in combination with the provision of rewards. In general, little
synergistic effects were found. Thus, there is a need to further
assess the cumulative effect of different system components on
treatment effectiveness and to analyze how specific
combinations affect behavior change. To fully determine the
most optimal delivery mode, further studies are needed that
randomly assign participants to different platforms for delivery
[128].
Although no consensus with respect to effectiveness of online
peer support was reached yet [94,95], anonymous online support
groups and discussion forums might help users to overcome the
feeling of being stigmatized by connecting patients with others.
A further advantage of these social support components is that
time and location are no longer obstacles for active participation
[96]. Nevertheless, further research is needed concerning the
type of online support, such as expert-led or user-driven,
moderated or nonmoderated, and synchronous (eg, chat rooms)
or asynchronous (eg, discussion forums) [96]. From a design
and engagement perspective, a nuanced view on target groups
suggests that complementarity between content of interventions
that target different mental disorders is crucial when designing
computerized and Web-based mental health interventions.
Patients with multiple disorders present a considerable challenge
in the design of technology-supported interventions and, as a
consequence, are often excluded from studies even though they
might profit from certain components. One reason for the
exclusion of comorbid and multimorbid patients is the pressure
toward relatively stringent and precisely defined interventions,
which are amenable to RCTs [130]. Regarding the high
comorbidity of mental disorders, it is of greatest importance
that this topic is given more attention in the design of
technology-amenable interventions.
Conclusions
The development of MHIS targeting the change in health
behavior requires great expertise and a thorough understanding
of the problem area, underlying therapeutic strategies, and the
design of persuasive systems. The findings of this systematic
review contribute to the body of knowledge on the effectiveness
of technology-supported therapeutic interventions for the
treatment of depressive symptoms. This work is intended to
provide a basis for the assessment of the impact of specific
system components on treatment effects in RCTs of technology-
and Web-based interventions for depression. Thus, the overall
goal of this review was to identify such components and to
enhance the understanding of the mechanisms through which
technology-enabled interventions exert their therapeutic benefits
by means of such.
Further quantitative studies are needed to assess the impact of
identified components and to identify other system components
that are relevant for the design of future technology-mediated
MHIS for the treatment of depression and other mental disorders.
Because of the high relevance of the anatomy of MHIS, attention
should be paid to design issues when developing new eHealth
services in the future. To enhance dissemination and utilization
of technology-based MHIS, the focus needs to be not only on
how the interventions affect users, but also on how patients use
and interact with technology and one another through them.
Therefore, future studies are needed that add to the body of
knowledge of technology-supported interventions for the
treatment of depression by assessing patterns of program usage
and user engagement.
To conclude, health information technology is a fast-growing
field of research, which has the potential to effectively treat
people suffering from mental disorders. Despite that, there is
still room for improvement in the design of technology-based
interventions for the treatment of depression. The delivery of
interventions via technology is a promising and cost-effective
approach to diminish the significant treatment gap and the
various barriers associated with the disorder.
J Med Internet Res 2017 | vol. 19 | iss. 5 | e191 | p.24http://www.jmir.org/2017/5/e191/
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Conflicts of Interest
None declared.
Multimedia Appendix 1
Subgroup analyses by study quality. Q>3.5: high quality studies; Q<= 3.5: low quality studies.
[PNG File, 333KB - jmir_v19i5e191_app1.png ]
Multimedia Appendix 2
Subgroup analyses by duration of treatment. Comparison of studies with a duration of equal or less than 10 weeks with trials of
more than 10 weeks duration.
[PNG File, 334KB - jmir_v19i5e191_app2.png ]
Multimedia Appendix 3
Subgroup analyses by face-to-face contact. Comparison of studies that made use of face-to-face therapy (F2F_Y) with those that
did not use live therapist contact (F2F_N).
[PNG File, 332KB - jmir_v19i5e191_app3.png ]
Multimedia Appendix 4
Subgroup analyses by the use of a CBT protocol. Comparison of interventions that were based on CBT (CBT_Y) with treatments
that did not make use of CBT techniques (CBT_N).
[PNG File, 333KB - jmir_v19i5e191_app4.png ]
Multimedia Appendix 5
Subgroup analyses by severity of baseline depression. Comparison of interventions that studied patients with a high level of
baseline depression (SEV_H) with trials that included patients with a low level of baseline severity (SEV_L).
[PNG File, 332KB - jmir_v19i5e191_app5.png ]
Multimedia Appendix 6
Subgroup analyses by age of included participants. Comparison of trials in A) adult patients (AGE_1) with B) trials that studied
effects in adults excluding older adults (AGE_2), C) trials in older adults only (AGE_3), and D) trials that studied the effect on
symptoms of depression in adolescents only (AGE_4).
[PNG File, 342KB - jmir_v19i5e191_app6.png ]
Multimedia Appendix 7
Subgroup analyses by type of comparator. Comparison of trials with a control group that received treatment as usual (TAU) with
RCTs that compared the intervention to a waiting list control (WLC) group.
[PNG File, 241KB - jmir_v19i5e191_app7.png ]
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Abbreviations
BDI: Beck Depression Inventory
CBT: cognitive behavior therapy
HIS: health information system
MHIS: mental health information system
PHQ: Patient Health Questionnaire
RCT: randomized controlled trial
SMD: standard mean difference
SMS: short message service
TAU: treatment as usual
WLC: waiting-list control
Edited by G Eysenbach; submitted 22.01.17; peer-reviewed by L Bijker, S Hermsen, T Aledavood, G Constantinescu; comments to
author 26.02.17; revised version received 10.03.17; accepted 06.04.17; published 31.05.17
Please cite as:
Wahle F, Bollhalder L, Kowatsch T, Fleisch E
Toward the Design of Evidence-Based Mental Health Information Systems for People With Depression: A Systematic Literature Review
and Meta-Analysis
J Med Internet Res 2017;19(5):e191
URL: http://www.jmir.org/2017/5/e191/
doi:10.2196/jmir.7381
PMID:
©Fabian Wahle, Lea Bollhalder, Tobias Kowatsch, Elgar Fleisch. Originally published in the Journal of Medical Internet Research
(http://www.jmir.org), 31.05.2017. This is an open-access article distributed under the terms of the Creative Commons Attribution
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