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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.
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.
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.
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,
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.
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” [
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 [
A recent preliminary literature review by Wahle and Kowatsch [
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” [
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.
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 [
For the implementation of this systematic review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was used [
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 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.
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
Keyword combinations used in the literature search process.
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.
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.
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-
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 [
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.
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 [
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.
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 collection tables were predeveloped and subsequently refined during the process of data extraction. The following information were collected from every article.
The occurrence of comorbidity was recorded. A difference was made between depression/depressive symptoms only and depression/depressive symptoms co-occurring with anxiety.
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).
Where applicable, all relevant information provided on the control condition was recorded.
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
Categorization of studies according to baseline depression severity and age of participants.
Item | Rating | |||
0 | No depression | |||
1-5 | Minimal depression | |||
6-9 | Mild depression | |||
10-14 | Moderate depression | |||
15-19 | Moderately severe | |||
≥20 | Severe depression | |||
0-13 | No depression | |||
14-19 | Minimal depression | |||
20-28 | Mild depression | |||
≥29 | Severe depression | |||
0 | Not reported | |||
1 | Mild/minimal to moderate | |||
2 | Moderately severe/severe | |||
3 | Moderately severe to severe/major depressive episode | |||
0 | No age restrictions | |||
1 | Adults (>16 years of age) | |||
2 | Adolescents (14-24 years of age) | |||
3 | Older adults (>50 years of age) | |||
4 | Adults without older adults (18-75 years of age) |
We recorded all relevant outcomes reported on at least one of the following scores: the BDI or PHQ score.
Study quality was assessed according to Jadad et al [
For a quantified overview, the individual system components were either binary coded or, if applicable, kept in original scale.
The quality of trials was examined according to the Jadad score [
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?
Study | 1 | 2 | 3 | 4 | 5 | 6 | Total score | Quality rating | |||||||||
Agyapong [ |
0.0 | 1.0 | 1.0 | 0.5 | 1.0 | 0.5 | 5.0 | Good | |||||||||
Andersson [ |
0.0 | 1.0 | 1.0 | 0.5 | 1.0 | 0.5 | 5.0 | Good | |||||||||
Andersson [ |
0.0 | 1.0 | 1.0 | 0.5 | 1.0 | 0.5 | 5.0 | Good | |||||||||
Berger [ |
0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.5 | 4.5 | Good | |||||||||
Burton [ |
0.0 | 1.0 | 1.0 | 0.5 | 1.0 | 0.5 | 5.0 | Good | |||||||||
Carlbring [ |
0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.5 | 4.5 | Good | |||||||||
Choi [ |
0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 | 4.0 | Fair | |||||||||
Clarke [ |
0.0 | 0.5 | 1.0 | 0.0 | 1.0 | 0.0 | 3.5 | Fair | |||||||||
de Graaf [ |
0.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | Fair | |||||||||
Holländare [ |
0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 2.5 | Poor | |||||||||
Høifødt [ |
0.0 | 1.0 | 1.0 | 0.5 | 1.0 | 1.0 | 5.5 | Good | |||||||||
Johansson [ |
0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 | 4.0 | Fair | |||||||||
Johansson [ |
0.0 | 1.0 | 1.0 | 0.5 | 0.0 | 0.0 | 3.5 | Fair | |||||||||
Kay-Lambkin [ |
0.0 | 0.5 | 1.0 | 0.5 | 1.0 | 1.0 | 5.0 | Good | |||||||||
Kessler [ |
0.0 | 0.5 | 1.0 | 0.5 | 1.0 | 0.0 | 4.0 | Fair | |||||||||
Kivi [ |
0.0 | 1.0 | 1.0 | 0.5 | 0.0 | 1.0 | 4.0 | Fair | |||||||||
Lappalainen [ |
0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 3.0 | Fair | |||||||||
Lappalainen [ |
0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | Poor | |||||||||
Ly [ |
0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 1.0 | 5.0 | Good | |||||||||
Meyer [ |
0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 | 4.0 | Fair | |||||||||
Meyer [ |
0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 | 4.0 | Fair | |||||||||
Morgan [ |
0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 | 4.0 | Fair | |||||||||
Moritz [ |
0.0 | 1.0 | 0.0 | 0.5 | 1.0 | 0.5 | 4.0 | Fair | |||||||||
Perini [ |
0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 | 4.0 | Fair | |||||||||
Phillips [ |
1.0 | 1.0 | 1.0 | 1.0 | 0.5 | 1.0 | 6.5 | Good | |||||||||
Preschl [ |
0.0 | 1.0 | 1.0 | 0.0 | 0.5 | 0.0 | 3.5 | Fair | |||||||||
Richards [ |
0.0 | 0.5 | 1.0 | 0.0 | 1.0 | 0.0 | 3.5 | Fair | |||||||||
Richards [ |
0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 | 3.5 | Fair | |||||||||
Ruwaard [ |
0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 | 4.0 | Fair | |||||||||
Sheeber [ |
0.0 | 1.0 | 1.0 | 1.0 | 0.0 | 1.0 | 5.0 | Good | |||||||||
Spek [ |
0.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | Fair | |||||||||
Ström [ |
0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 | 4.0 | Fair | |||||||||
Titov [ |
0.0 | 0.5 | 1.0 | 0.0 | 1.0 | 0.0 | 3.5 | Fair | |||||||||
Titov [ |
0.0 | 1.0 | 1.0 | 0.5 | 1.0 | 0.0 | 4.5 | Good | |||||||||
Vernmark [ |
0.0 | 1.0 | 1.0 | 0.5 | 1.0 | 1.0 | 5.5 | Good | |||||||||
Wagner [ |
0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 | 4.0 | Fair | |||||||||
Watts [ |
0.0 | 0.5 | 1.0 | 0.0 | 0.0 | 0.0 | 2.5 | Poor | |||||||||
Agyapong [ |
0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 | 4.0 | Fair | |||||||||
Agyapong [ |
0.0 | 1.0 | 1.0 | 0.5 | 1.0 | 1.0 | 5.5 | Good | |||||||||
Andersson [ |
0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 | 4.0 | Fair | |||||||||
Andersson [ |
0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 | 4.0 | Fair | |||||||||
Berger [ |
0.0 | 1.0 | 1.0 | 0.5 | 1.0 | 0.0 | 4.5 | Good | |||||||||
Agyapong [ |
0.0 | 1.0 | 0.0 | 0.0 | 1.0 | 0.0 | 3.0 | Fair | |||||||||
Andersson [ |
0.0 | 1.0 | 1.0 | 0.0 | 0.0 | 0.0 | 3.0 | Fair | |||||||||
Andersson [ |
0.0 | 1.0 | 1.0 | 0.0 | 1.0 | 0.0 | 4.0 | Fair |
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 [
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 [
Summary of study characteristics, including location, sample size, study name, severity, and age of participants (N=45).
Study | Location | N | Name | Severity | Age | |
Agyapong [ |
Ireland | 54 | No name | Moderately severe to severe | Adults (≥16 years) | |
Andersson [ |
Sweden | 69 | No name | Mild to severe | Adults (≥16 years) | |
Andersson [ |
Sweden | 117 | No name | Mild to moderate | Adults (≥16 years) | |
Berger [ |
Switzerland Germany | 76 | Deprexis | Mild to severe | Adults (≥16 years) | |
Burton [ |
Romania Spain Scotland UK | 28 | Help4Mood | Mild to severe | Adults without older adults (18-75 years) | |
Carlbring [ |
Sweden | 80 | Depressions-hjälpen | Mild to moderate | Adults (≥16 years) | |
Choi [ |
Australia | 63 | Brighten Your Mood Program | Minimal to moderately severe | Adults (≥16 years) | |
Clarke [ |
USA | 160 | No name | NR | Adolescents (14-24 years) | |
de Graaf [ |
Netherlands | 303 | Colour Your Life | Mild to moderate | Adults without older adults (18-75 years) | |
Holländare [ |
Sweden | 84 | No name | Mild | Adults (≥16 years) | |
Høifødt [ |
Norway | 106 | MoodGYM (Norwegian Version) | Moderate to severe | Adults without older adults (18-75 years) | |
Johansson [ |
Sweden | 92 | SUBGAP | Mild to severe | Adults (≥ 16 years) | |
Johansson [ |
Sweden | 121 | No name | Mild to severe | Adults (≥ 16 years) | |
Kay-Lambkin [ |
Australia | 97 | SHADE | Mild to severe | Adults (≥ 16 years) | |
Kessler [ |
UK | 297 | No name | Mild to severe | Adults without older adults (18-75 years) | |
Kivi [ |
Sweden | 92 | Depressions-hjälpen | Mild to moderate | Adults (≥16 years) | |
Lappalainen [ |
Finland | 39 | Good Life Compass | Mild to severe | Adults (≥16 years) | |
Lappalainen [ |
Finland | 38 | Good Life Compass | Mild to severe | Adults (≥16 years) | |
Ly [ |
Sweden | 93 | No name | Mild to severe | Adults (≥16 years) | |
Meyer [ |
Germany | 163 | Deprexis | Moderately severe to severe | Adults without older adults (18-75 years) | |
Meyer [ |
Germany | 396 | Deprexis | NR | Adults (≥16 years) | |
Morgan [ |
UK Australia Canada Ireland New Zealand USA | 1736 | Mood Memos | Mild to severe | Adults (≥16 years) | |
Moritz [ |
Germany | 210 | Deprexis | NR | Adults without older adults (18-75 years) | |
Perini [ |
Australia | 48 | Sadness | Mild to severe | Adults (≥16 years) | |
Phillips [ |
UK | 637 | MoodGym | NR | Adults (≥16 years) | |
Preschl [ |
Switzerland | 40 | E-mental Health Butler System | Minimal to moderate | Older adults (≥50 years) | |
Richards [ |
Ireland | 262 | Space from Depression | Mild to moderate | Adults (≥16 years) | |
Richards [ |
Ireland | 101 | Beating the Blues | Mild to moderate | Adolescents (14-24 years) | |
Ruwaard [ |
Netherlands | 54 | No name | Minimal to moderate | Adults (≥16 years) | |
Sheeber [ |
USA | 70 | Mom-Net | NR | No age restrictions | |
Spek [ |
Netherlands | 301 | Lewinsohn’s Coping With Depression Course | Subthreshold depression | Older adults (≥50 years) | |
Ström [ |
Sweden | 48 | No name | Mild to moderate | No age restrictions | |
Titov [ |
Australia | 54 | Managing Your Mood Course | Mild to moderate | Older adults (≥50 years) | |
Titov [ |
Australia | 141 | SADNESS | Mild to severe | Adults (≥16 years) | |
Vernmark [ |
Sweden | 88 | No name | mild to moderate | Adults (≥16 years) | |
Wagner [ |
Switzerland | 62 | No name | Minimal to severe | Adults (≥16) | |
Watts [ |
Australia | 52 | Get Happy (Mobile app of the sadness program) | Mild to moderate | Adults (≥16 years) | |
Johansson [ |
Sweden | 57 | No name | Moderate to severe | Adults (≥16 years) | |
Mullin [ |
Australia | 31 | UniWellbeing Course | Minimal to moderate | Adults (≥16 years) | |
Newby [ |
Australia | 109 | Worry and Sadness Program | Mild to severe | Adults (≥16 years) | |
Proudfoot [ |
UK | 167 | Beating the Blues | NR | Adults without older adults (18-75 years) | |
Titov [ |
Australia | 290 | Transdiagnostic Wellbeing Course (TD-CBT) or Disorder-Specific Mood Course (DS-CBT) | Mild to severe | Adults without older adults (18-75 years) | |
Titov [ |
Australia | 93 | Wellbeing Course | Moderate to severe | Adults (≥16 years) | |
Titov [ |
Australia | 38 | Wellbeing Course | Mild to severe | Adults (≥16 years) |
Summary of the study treatment and control groups and their relevant scores at baseline and follow-up (N=45).
Study | Treatment groupa | Control groupa | Baseline | Follow-up | |||||||||
Treatment | Control | Wks | Treatment | Control | |||||||||
n | Mean (SD) | n | Mean (SD) | n | Mean (SD) | n | Mean (SD) | ||||||
Agyapong [ |
Supportive text messages sent by a computer + TAU | Thank you text message + TAU | 26 | 31.58 (7.70) | 28 | 31.99 (9.50) | 13 | 24 | 8.60 (7.90) | 26 | 16.60 (9.80) | ||
Andersson [ |
Guided Web-based CBT | Group CBT | 33 | 24.00 (7.70) | 36 | 25.30 (6.60) | 9 | 32 | 13.60 (10.10) | 33 | 17.90 (8.80) | ||
Andersson [ |
Web-based CBT+ Web-based discussion group | Web-based discussion group only | 36 | 20.50 (6.70) | 49 | 20.90 (8.50) | 10 | 36 | 12.20 (6.80) | 49 | 19.50 (8.10) | ||
Berger [ |
Low-intensity therapist-guided, computerized CBT | WLC | 25 | 28.80 (8.20) | 26 | 29.80 (8.60) | 10 | 25 | 17.30 (10.20) | 22 | 28.50 (9.40) | ||
Burton [ |
Help4Mood (Self-report and biometric monitoring + elements of CBT) + TAU | TAU | 14 | 19.60 (8.10) | 13 | 21.80 (6.80) | 4 | 12 | 13.90 (8.10) | 9 | 17.60 (6.80) | ||
Carlbring [ |
Web-based behavioral activation and acceptance-based treatment | WLC | 40 | 26.32 (5.97) | 40 | 25.13 (5.19) | 8 | 40 | 16.65 (8.04) | 38 | 23.43 (7.67) | ||
Choi [ |
Web-based CBT | WLC | 28 | 25.76 (8.53) | 30 | 20.83 (7.58) | 8 | 23 | 13.48 (9.28) | 28 | 21.27 (7.86) | ||
Clarke [ |
Web-based, pure self-help CBT | TAU | 83 | 10.00 (0.80) | 77 | 10.30 (0.80) | 5 | 58 | 9.10 (0.70) | 58 | 10.10 (0.70) | ||
de Graaf [ |
Computerized CBT | TAU | 100 | 28.20 (7.70) | 103 | 27.90 (7.50) | 9 | 95 | 20.60 (10.40) | 97 | 22.10 (10.20) | ||
Holländare [ |
Guided, Web-based CBT | Nonspecific support by an online therapist (email contact) + monthly rating of their depressive symptoms using the MADRS-S | 42 | 17.00 (11.50) | 42 | 17.70 (11.50) | 10 | 38 | 9.30 (12.00) | 39 | 13.40 (11.90) | ||
Høifødt [ |
Guided, Web-based CBT (TAU) | WLC (TAU) | 52 | 21.13 (6.85) | 54 | 22.27 (6.74) | 7 | 52 | 14.20 (8.15) | 54 | 18.63 (8.64) | ||
Johansson [ |
Web-based psychodynamic psychotherapy + online therapist contact | Web-based structured support intervention (psychoeducation and scheduled weekly contacts online) | 46 | 26.54 (5.80) | 46 | 26.33 (6.70) | 10 | 42 | 11.48 (7.80) | 46 | 20.22 (7.80) | ||
Johansson [ |
Tailored Web-based CBT | Web-based discussion group with weekly discussion themes related to depression and the treatment of depression | 36 | 26.44 (7.60) | 42 | 26.24 (7.90) | 10 | 36 | 13.78 (9.40) | 39 | 21.67 (9.50) | ||
Kay-Lambkin [ |
Computerized CBT therapy and motivational interviewing by a computer program | No further treatment | 23 | 28.57 (9.89) | 21 | 32.86 (9.59) | 13 | 23 | 17.09 (12.14) | 21 | 22.95 (10.46) | ||
Kessler [ |
Web-based CBT + TAU | WLC (TAU) | 149 | 32.80 (8.30) | 148 | 33.50 (9.30) | 17 | 113 | 14.50 (11.20) | 97 | 22.00 (13.50) | ||
Kivi [ |
Web-based CBT | TAU | 45 | 25.50 (7.87) | 47 | 26.09 (9.39) | 13 | 30 | 13.23 (10.94) | 35 | 14.46 (9.88) | ||
Lappalainen [ |
Guided Web-based acceptance and commitment therapy without face-to-face contact | WLC | 19 | 22.11 (7.79) | 20 | 20.65 (6.80) | 7 | 18 | 13.34 (6.75) | 20 | 17.85 (7.34) | ||
Lappalainen [ |
Guided Web-based acceptance and commitment therapy | Face-to-face Acceptance and Commitment therapy (ACT) | 19 | 20.79 (9.34) | 19 | 23.11 (6.38) | 6 | 19 | 10.26 (8.20) | 18 | 9.17 (5.24) | ||
Ly [ |
Blended treatment (4 face-to-face sessions + a smartphone application used between sessions) | Full behavioral activation | 46 | 28.96 (8.07) | 47 | 27.32 (7.89) | 9 | 44 | 15.17 (11.51) | 46 | 13.43 (11.27) | ||
Meyer [ |
Web-based CBT + TAU | WLC (TAU) | 78 | 16.62 (3.44) | 85 | 17.20 (3.86) | 9 | 60 | 10.08 (6.37) | 73 | 13.64 (6.14) | ||
Meyer [ |
Web-based CBT + TAU | WLC (TAU) | 320 | 26.72 (9.86) | 76 | 27.11 (8.98) | 9 | 159 | 19.87 (11.85) | 57 | 27.15 (10.01) | ||
Morgan [ |
Emails promoting the use of self-help strategies | Emails containing depression information | 862 | 16.40 (5.98) | 874 | 16.90 (5.76) | 6 | 273 | 10.80 (6.84) | 301 | 11.50 (6.72) | ||
Moritz [ |
Web-based CBT | WLC | 105 | 28.81 (11.11) | 105 | 30.02 (10.18) | 8 | 80 | 20.51 (12.22) | 90 | 25.67 (11.65) | ||
Perini [ |
Guided Web-based CBT | WLC | 27 | 27.30 (7.30) | 18 | 27.24 (6.18) | 8 | 27 | 17.30 (9.86) | 17 | 23.33 (9.29) | ||
Phillips [ |
Computer-based CBT | Attention control (5 websites with general information about mental health) | 311 | 14.60 (5.40) | 318 | 14.60 (5.60) | 6 | 164 | 9.90 (6.10) | 176 | 10.20 (6.00) | ||
Preschl [ |
Face-to-face life-review therapy including computer supplements | WLC | 20 | 19.00 (6.60) | 16 | 16.50 (5.60) | 8 | 20 | 10.00 (6.30) | 16 | 15.10 (7.80) | ||
Richards [ |
Guided Web-based CBT | WLC | 96 | 20.90 (3.83) | 92 | 20.84 (4.17) | 8 | 96 | 15.67 (7.68) | 92 | 20.43 (6.97) | ||
Richards [ |
Unguided Web-based CBT | Therapist-assisted email CBT | 43 | 21.72 (5.30) | 37 | 22.70 (4.70) | 8 | 21 | 12.81 (6.90) | 25 | 11.52 (4.90) | ||
Ruwaard [ |
Guided Web-based CBT | WLC | 36 | 19.70 (5.50) | 18 | 21.30 (5.30) | 11 | 36 | 9.80 (6.50) | 18 | 15.60 (7.60) | ||
Sheeber [ |
Guided Web-based CBT | WLC (TAU) | 35 | 26.20 (9.80) | 35 | 25.40 (9.00) | 14 | 34 | 13.40 (10.40) | 35 | 22.50 (11.00) | ||
Spek [ |
Unguided Web-based CBT | Group face-to-face CBT | 102 | 19.17 (7.21) | 99 | 17.89 (9.95) | 10 | 67 | 11.97 (8.05) | 56 | 11.43 (9.41) | ||
Ström [ |
Therapist-guided Web-based physical activity (guided self-help program) | WLC | 24 | 26.92 (9.30) | 24 | 28.25 (7.08) | 9 | 24 | 17.88 (11.30) | 24 | 24.04 (6.86) | ||
Titov [ |
Guided Web-based CBT | WLC | 27 | 11.04 (5.62) | 25 | 12.04 (5.42) | 8 | 23 | 3.96 (2.48) | 22 | 12.68 (5.48) | ||
Titov [ |
Clinician-guided Web-based CBT | WLC | 41 | 27.15 (9.96) | 40 | 26.33 (10.46) | 8 | 41 | 15.29 (9.81) 7.59 (4.04) | 40 | 26.15 (10.14) | ||
Vernmark [ |
Web-based CBT, email supported | WLC | 30 | 22.20 (5.30) | 29 | 21.80 (6.60) | 8 | 29 | 10.30 (5.20) | 29 | 16.60 (7.90) | ||
Wagner [ |
Guided Web-based CBT | Face-to-face CBT | 32 | 22.96 (6.07) | 30 | 23.41 (7.63) | 8 | 25 | 12.41 (10.03) | 28 | 12.33 (8.77) | ||
Watts [ |
Smartphone-based CBT | Computer-based CBT | 15 | 33.46 (2.95) | 20 | 30.90 (2.55) | 8 | 1010 | 12.53 (3.26) | 15 | 13.68 (2.79) | ||
Johansson [ |
Web-based psychodynamic, guided self-help treatment based on affect-phobia therapy (APT) | WLC | 28 | 15.32 (3.30) | 29 | 15.07 (4.40) | 10 | 28 | 5.89 (2.80) | 29 | 10.59 (6.40) | ||
Mullin [ |
Web-based CBT (total sample: N=30); clinical subsample (PHQ-9 ≥10) | WLC | 20 | 14.10 (3.62) | 11 | 14.63 (3.35) | 6 | 20 | 8.33 (4.86) | 11 | 13.37 (7.42) | ||
Newby [ |
Guided Web-based CBT (Worry and Sadness Program) | WLC | 46 | 21.24 (6.98) | 54 | 22.41 (9.17) | 10 | 43 | 10.48 (8.30) | 53 | 21.24 (10.56) | ||
Proudfoot [ |
Computerized CBT | TAU | 53 | 25.38 (11.05) | 53 | 24.08 (9.78) | 8 | 47 | 12.04 (10.45) | 50 | 18.36 (12.65) | ||
Titov [ |
Clinician-guided Web-based CBT | Self-guided Web-based CBT | 112 | 15.07 (3.57) | 105 | 15.23 (3.85) | 8 | 112 | 7.36 (5.04) | 105 | 8.44 (5.14) | ||
Titov [ |
Transdiagnostic self-guided Web-based CBT with automated email | self-guided Web-based CBT without automated email | 47 | 14.64 (3.34) | 46 | 14.39 (3.33) | 8 | 47 | 7.58 (4.60) | 46 | 10.57 (6.16) | ||
Titov [ |
Web-based CBT with email and phone support | WLC | 18 | 14.39 (4.27) | 20 | 13.35 (6.25) | 10 | 18 | 7.67 (5.97) | 20 | 12.15 (4.93) |
a BDI: Beck Depression Index; CBT: cognitive behavioral therapy; PHQ: Patient Health Questionnaire; TAU: treatment as usual; WLC: waiting-list control.
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 [
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 [
We calculated the effect size (SMD or Hedges’
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.
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 test for heterogeneity [
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.
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 [
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.
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.
Flow diagram of study selection.
The risk of bias at the level of the individual trials is addressed in
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 [
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 [
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” [
Research by Agyapong et al [
Although no consensus with respect to effectiveness of online peer support was reached yet [
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 [
Stemming from Oinas-Kikkonens concept of reminders, Whitton et al [
Because a participant’s preferred medium for reading might be paper [
Homework assignments, as commonly used in standard care [
Tracking symptoms, either objectively using sensors [
Diaries form a way of self-monitoring and self-reflection and are frequently used in classical forms of CBT [
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 [
A recent experimental study found that audience feedback is a valuable tool to enhance users’ perceptions of health-related YouTube clips [
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 [
The use of game-like strategies has demonstrated to produce positive outcomes in previous studies of technology-based health interventions [
Virtual agents or avatars could be used for persuasive purposes and to support self-management among patients [
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 [
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 [
Although social support is widely recognized as an important feature in behavior change [
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
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
Study | HIS channel | Tailoring | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | |
Agyapong [ |
Mobile phone | 0/NR | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |
Andersson [ |
Online | 0/NR | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |
Andersson [ |
Online | Partly | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | |
Berger [ |
Online | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | |
Burton [ |
Online | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | |
Carlbring [ |
1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | ||
Choi [ |
Online | 0/NR | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | |
Clarke [ |
Online | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | |
de Graaf [ |
Online | 0/NR | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | |
Holländare [ |
Online | 0/NR | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | |
Høifødt [ |
Online | Feedback/reminders | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | |
Johansson [ |
Online | Feedback/reminders | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |
Johansson [ |
Online | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |
Kay-Lambkin [ |
Computer program | 0/NR | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | |
Kessler [ |
Online | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
Kivi [ |
Online | Feedback/reminders | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | |
Lappalainen [ |
Online | Feedback/reminders | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | |
Lappalainen [ |
Online | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | |
Ly [ |
Mobile phone | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | |
Meyer [ |
Online | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | |
Meyer [ |
Online | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | |
Morgan [ |
0/NR | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ||
Moritz [ |
Online | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | |
Perini [ |
Online | 0/NR | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | |
Phillips [ |
Online | Feedback/reminders | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | |
Preschl [ |
Computer program | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | |
Richards [ |
Online | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | |
Richards [ |
Online | Feedback /reminders | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | |
Ruwaard [ |
Online | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | |
Sheeber [ |
Online | Feedback/reminders | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | |
Spek [ |
Online | 0/NR | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | |
Ström [ |
Online | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |
Titov [ |
Online | 0/NR | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |
Titov [ |
Online | 0/NR | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | |
Vernmark [ |
Online | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | |
Wagner [ |
Online | Feedback/reminders | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |
Watts [ |
Mobile phone vs PC | 0/NR | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | |
Agyapong [ |
Online | 0/NR | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |
Johansson [ |
Online | Feedback/reminders | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | |
Mullin [ |
Online | 0/NR | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | |
Newby [ |
Online | 0/NR | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |
Proudfoot [ |
Computer program | 1 | NR | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | |
Titov [ |
Online | 0/NR | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | |
Titov [ |
Online | 0/NR | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | |
Titov [ |
Online | 0/NR | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
a Rating system is 1=component present and 0=component not present. CG: control group; NR: not reported; TG: treatment group
System component distribution of included studies (N=45).
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;
Effect of technology-based interventions on symptoms of depression in included studies (N=45).
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 (
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 (
Risk of bias across all included studies (N=45).
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
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,
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,
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,
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,
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,
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,
Comparing technology-based treatments to TAU resulted in a moderate effect (SMD –0.48, 95% CI –0.78 to –0.18,
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 [
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 [
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 [
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 [
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 [
Doherty et al [
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 [
Although social support is widely recognized as an important feature in behavior change [
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 [
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 [
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 [
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 [
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 [
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 [
Although clinicians tend to be very self-protective about their time commitments and skeptical about technology [
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 [
In addition, little is known about the synergistic effects of behavior-change components, modes of delivery, and user friendliness. Van Genugten et al [
Although no consensus with respect to effectiveness of online peer support was reached yet [
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.
Subgroup analyses by study quality. Q>3.5: high quality studies; Q<= 3.5: low quality studies.
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.
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).
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).
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).
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).
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.
Beck Depression Inventory
cognitive behavior therapy
health information system
mental health information system
Patient Health Questionnaire
randomized controlled trial
standard mean difference
short message service
treatment as usual
waiting-list control
None declared.