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Psychiatr Clin North Am. Author manuscript; available in PMC Mar 1, 2012.
Published in final edited form as:
PMCID: PMC3057391

Internet-Based Depression Prevention over the Life Course: A Call for Behavioral Vaccines



Major depression is the number one cause of disability worldwide.1 The lifetime prevalence of major depression in the United States is 16.6%, but is as high as 19.8% for those in more recent birth cohorts.2 Depression, known as a “life course” disorder, usually has its first onset in mid-adolescence, and recurs every five to seven years in 80% of individuals.3 Most people with major depression do not obtain treatment4; of those who do obtain evidence-based treatments, approximately one-third do not improve.56 Depression is associated with substantial impairment both during and after the episode. Many who suffer from depression face difficulties in various areas, including school, interpersonal relationships, occupational adjustment, and behavior (e.g., substance abuse, suicide attempts). Depression affects both the individual and the nation, with a loss of $77.4 billion dollars/year in the United States alone attributed to depression.79 10

Need for preventive interventions

There are 121 million people with depression worldwide, yet fewer than 25% of them have access to effective treatments, and many of those in the developed world who do have access do not seek treatment.11 For example, even in study settings such as Youth Partners in Care (YPIC) where youth received a comprehensive primary care intervention to facilitate treatment, less than one-third of depressed adolescents in primary care attended counseling referrals to mental health specialists.12 Even within structured study settings, most individuals do not make full functional recoveries, warranting use of both effective treatment and prevention strategies.1314 Prevention-focused efforts have the potential to make a major impact on global public health, particularly if they are highly scalable and easily disseminated.13 Two recent meta-analyses indicate that clinical episodes of depression can be prevented, with a demonstrated mean incident rate ratio of 0.78 (22% risk reduction) for all interventions.15 16

Value of Technology-Based Approaches in Preventing Depression

The Institute of Medicine (IOM) has published two major reports on the prevention of mental, emotional, and behavioral disorders1819 and has called for the development and rigorous evaluation of new technology based on new prevention strategies. Technology-based delivery offers considerable benefits including easy access (time and space), patient autonomy, and “non-consumable" services that are autonomous from traditional (face-to-face) interventions.13, 20 In the IOM model (Figure 1, interventions are deployed to prevent the onset of illness in individuals who do not already meet diagnostic criteria for these disorders. Such interventions can be universal (applied to the entire population), selected (applied to those with a risk factor for disorder), and indicated (applied to those with early symptoms of disorder). Ritterband and others have summarized key components for Internet interventions, such as the importance of the motivation of participants, participant use of interventions, website design, and support for participation.2123 However, technology-based interventions have important limitations, such as the difficulty motivating participants to completion, stigma, and limited availability of Internet access in economically distressed settings.2425

Figure 1
Institute of Medicine Prevention Diagram.

"Behavioral Vaccine" Model

Effective vaccine strategies include several key components that relate closely to Internet-based prevention (Figure 2, These elements include: (1) a schedule of vaccines across the lifespan to address specific threats; (2) an active ingredient (e.g., an antigen or live attenuated organism) that is intended to protect against the disease; (3) an adjuvant to boost the immune system's natural response to active components; and (4) a structured implementation and delivery schedule that optimizes immunity We have previously worked to develop Internet interventions based on the frameworks proposed by Nation26 and Wandersman27 for conventional, face-to-face effective community-based interventions,2829 and propose to integrate these models into a “behavioral vaccine model” which we believe is aptly applicable to technology-based delivery. Like vaccines, “behavioral vaccines” require four key components: (1) a life course schedule that is theory-driven and includes booster doses; (2) information and training (active components) to encode responses to future threats that is theoretically and empirically grounded; (3) a motivational framework to boost response to behavior prescription (“motivation”, “positive relationships”, and “dose”); and (4) a structured implementation strategy, as we must intervene before the onset of disorder (“website design”, “varied teaching methods”, “effective training”, “practitioner control.”) 23, 2627

Figure 2
Biological and Behavioral Vaccines across the Life Course with some example interventions.

Presently, we will review the literature on Internet-based depression prevention programs using this "behavioral vaccine" development model, reviewing literature relevant to each component of the model in turn. Specifically, we will look at literature on effective components of Internet-based depression prevention programs across the life-course, followed by a review of frameworks for motivation. Finally, we will discuss the literature pertaining to implementation strategies associated with these Internet-based prevention programs. Cohen’s D effect sizes (based on self-report symptoms) and number needed to treat (based on clinical assessments) were calculated for each age group.

The studies reviewed here were identified through a systematic search of Medline and PsycInfo databases for unique Internet depression intervention papers that met our inclusion criteria. The great majority of papers identified did not meet the IOM criteria for a prevention study. Most of the studies were best characterized as “early intervention,” encompassing mixed populations of those just below (indicated prevention) and above (case identification treatment) diagnostic threshold. Because only a limited number of true “prevention” studies are available for review, we included early intervention studies that reported at least 6 months of follow-up to provide a broad overview of this field.

Life Course Schedule

The first component of an effective vaccine is a life-course schedule that is theory driven. We review interventions in light of timing, life-course stage, and theoretical grounding of the intervention components (e.g. problem-solving therapy versus cognitive behavioral psychotherapy).

Interventions involving Mothers, Infants and Children

While we did not identify a published intervention, we are aware of several unpublished projects that suggest the prospect of progress in this life course period, including Mothers and Babies, Legend of the Snow Orchid, and the Pennsylvania Optimism Study, for which some limited information is available.

The Mothers and Babies Internet Project (HealthyPregnancy.ucsf.edu and EmbarazoSaludable.ucsf.edu) is an unpublished pilot randomized controlled trial (RCT) examining an intervention intended to prevent postpartum depression in an international sample of pregnant women. The program utilizes an automated, self-help, web-adapted, cognitive behavioral therapy (CBT)-based intervention30 and recruited participants online using sponsored links over a six month period (January – July 2009). Participants were subsequently stratified based on depression status as baseline (“prevention” or “treatment” – depending on whether they are above or below the diagnostic threshold) and randomized to either the Mothers and Babies or information condition. Using automated email messages, depression status was assessed monthly up to six months postpartum, with subsequent deployment of a CBT-based intervention components focusing on reducing risk of depression during the peri-partum transition. The sample, currently N>1000, consisted of primarily Latino women (81%) from 45 countries in their late twenties (M= 27.6 years, SD = 5.6). Half of the participants were married (50%), the majority were educated (83% completed college degrees or above), most were employed (60%) and many had no history of major depressive episodes (69%). Another novel project is the Legend of the Snow Orchid Internet site, (https://www.roc-n-ash.com/imheportal/welcome/) developed by Dr. Daniel Fung for the Department of Mental Health Singapore. The site uses a fantasy-based game to reduce anxiety and depressed mood.31 The Pennsylvania Optimism Study program has been developed and deployed in multiple settings. The program focuses on creating a more favorable attributional style and plans are underway to develop an Internet application.32

Adolescent and Emerging Adulthood Studies

Six Internet-based studies were identified that focused on the prevention of depression in adolescents and/or emerging adults. Combined, these sites observed a mean effect size of 0.32 between randomization groups, and a pre/post effect size of 0.65 at the latest follow-up.3338

In a study of a problem-solving therapy (PST), which currently has no available data, approximately 210 participants between the ages of 12 and 18 with mild to moderate depressive symptoms will be randomized into an active group and a control group.34 This PTS intervention uses a step-by-step approach to first triage problems based on their importance, and then to generate, implement, and evaluate trial solutions. The control group will receive no treatment except a link to a website with general information about depression and anxiety. The active group will undergo a PST Internet intervention for a total of 5 weeks, and follow-up assessments will occur at 3 weeks, 5 weeks, 4 months, and 8 months.

The program MoodGYM (moodgym.anu.edu.au) consists of five modules, ranging between 30–45 minutes each, that focus on prevention, education, and treatment using CBT. An observational study of 2909 registrants (mean age=35.5, SD=13) and 71 university students demonstrated that the participants who completed more modules exhibited the best results, suggesting that more time on the site yields better outcomes. Its effectiveness in preventing depression was demonstrated through three randomized clinical trials that compared MoodGYM to the participation in a health class, using a universal prevention model. Initial early intervention studies were completed in two single gender schools (baseline depressed: male=19.5%; female=25%). In the single gender study, males showed a lower likelihood of becoming depressed, and in the female-only study, those in the intervention group were less depressed than the control group at 20 weeks (moderate effect size).36, 39 In the larger RCT (N=1477, mean age=14.34; SD=0.75; baseline depressed=10.1%), the effectiveness of MoodGYM was compared to that of a wait-list control group who received a typical school curriculum's health class. Only male participants showed a reduction in risk of depression (36.9% intervention, 48.5% control).33

The website Project CATCH-IT (http://catchit-public.bsd.uchicago.edu) provides an alternate source of depression intervention for youth. Project CATCH-IT is a primary care-based website that focuses on the prevention of depression in adolescents through the integration of CBT, interpersonal psychotherapy (IPT), and Behavioral Activation (BA). The website interacts with its users through homework assignments and short narratives. A pilot study of Project CATCH-IT with participants who were emerging adults (age range =18–24 years) demonstrated moderate effect sizes (group effect=0.81) for the site post-intervention. In a preventative RCT study of Project CATCH-IT, 84 participants were recruited via screening in the primary care setting (sub-threshold depressed mood to enter the study, indicated prevention model) and randomized into either a motivational interview group (MI) or a brief advice group as preparation for the Internet component. Those included had a mean age of 17.39 years (sd=2.04) and were from mostly moderate to high income families. The MI group visited the study website more often and had significantly fewer depressive episodes at the 3-month follow-up. 3738

Clarke et al. (2009) conducted an early-intervention (includes those above and below diagnostic threshold) RCT of 160 participants (80% female, mean age = 22.5 (SD=2.5), 44.9% depressed at baseline). The unguided program consisted of 4 main sections with interactive exercises and a CBT tutorial based on the Coping with Depression Course. Internet intervention participants were recruited from a common Health Maintenance Organization (HMO), and demonstrated moderate reductions in depressed mood compared to treatment as usual at 32 weeks after enrollment.40

Middle Adult

Nine Internet-based studies were identified that focused on middle-aged adults (mean age=41.8 years), with a mean between group effect size of 0.30.4150 BluePages (http://bluepages.anu.edu/au) is a depression literacy website that was designed to provide evidence-based information and treatment to a youth population. An early-intervention study (individuals above and below diagnostic threshold) was conducted to compare the results of using BluePages or MoodGYM. The study consisted of 525 participants (71% female) with a mean age of 36.43 (SD= 9.40). The participants were randomized into three groups: MoodGYM, BluePages, and an attention placebo consisting of a weekly interview to discuss lifestyle factors. Both sites were effective in reducing symptoms of depression, showing similar results, but those with baseline Center for Epidemilogical Studies Depression Scales (CES-D) scores above 16 evidenced significantly higher effect sizes.43 47

In a separate study, 299 adults (mean age= 43.3), 74.6% depressed at baseline, were used to measure the effectiveness of an Internet CBT program, ODIN (includes both focus on pleasurable activities and changing pessimistic cognitions), compared to a control group that received access to the Kaiser Permanente Online home page that provided non-interactive information about health concerns. The computer-generated invitation model included a mixed depressed/non-depressed sample (separate samples generated by query of health records), which could be described as early intervention. Of these participants (73% female), 74.6% were depressed at baseline. The 6-month follow-up provided a moderate pre/post effect size, but demonstrated a small between-group effect size compared to the control group. (However, many individuals in both groups were under active treatment.)45

Colour Your Life (CYL) is a multimedia, interactive computer program for depression that was intended to intervene to reduce depressed mood in a heterogenous population (some above and below diagnostic thresholds, early intervention). The program models an existing CWD course in Lewinsohn, which includes CBT-based lessons and incorporates a homework assignment following each of the nine 30-minute lessons. Originally, CYL was designed for a targeted age group of those over-50, but it was later adapted to suit a more expansive adult population (18–65 years). To test the program, 303 participants between 18 and 65 years (M= 44, SD= 11.6) were divided into CYL, treatment as usual (TAU), or CYL+TAU. This was an early-intervention study (mean baseline Beck Depression Index score= 27.8) where 21% of the population had no previous major depressive episodes. The results showed medium to large improvement effect sizes in all three groups at the six-month follow-up.46

Cavanagh studied the effectiveness of a self-help online CBT program called Beating the Blues, which consisted of an introductory video and nine 50-minute treatment sessions with corresponding homework assignments (computer based approach in clinic at that time).51 In an observational study, 219 participants (mean age= 43, 60% female), 32% exhibiting depression symptoms, tested the site and showed significant improvements that were sustained after 6 months.42 A separate study was conducted on Beating the Blues using a sample size of 310 participants (mean age= 44.7) with an average of 1.9 episodes of depression pre-treatment. The researchers compared the effectiveness of this program with a TAU control group comprised of patients who saw general practitioners for anxiety and/or depression (above and below diagnostic thresholds) recruited from seven general practices in London and South-east England. The results showed a significant decrease in scores for depression and anxiety among those assigned to the Beating the Blues condition, and the average of both depression and anxiety scores fell to the near-normal range.50

In a larger early-intervention study by Andersson, 117 adults (74% female) with a mean age of 36.1 years (SD= 10.57) and a baseline BDI score of 21.0 were randomized into either a CBT intervention or control group.41 The active group was directed through email to undergo an Internet self-help CBT program consisting of 5 modules with a total of 89 pages of text and a quiz following each module. Both groups exhibited a reduction in depression symptoms, but the active group showed greater improvements in their mood.41

In a prevention RCT (263 participants, age 18 and older), CBT and PST programs were compared alongside a wait-list control group who received no treatment until three months after the intervention was completed. Both treatments differed in length (CBT= 8 weeks, PST= 5 weeks), but had similar design formats; only the CBT site offered video and audio options for its users. Both programs rendered moderate to high effect sizes immediately post-intervention, which increased at the 12-month follow-up.52

Older Adult

Only one Internet-based depression prevention study targeting older adults was identified. In an early-intervention study conducted by Spek and colleagues (2007), 301 participants (63.5% female) (mean BDI baseline score= 18.4), with a mean age of 55 (SD=4.6), were randomly assigned to one of three groups: an Internet-based CBT intervention created at the Trimbos University in the Netherlands, a face-to-face CBT group, and a waiting-list control. The CBT site consisted of eight weekly modules comprised of text, exercises, videos, and figures. The intervention produced moderate between-group effect sizes at both post-intervention and after the 12-month follow-up.5354

Effective Components

In order to gauge the effectiveness of internet-based behavioral vaccines, we first review the active components of these prevention programs by comparing results to similar, more traditional, face-to-face interventions. More specifically, we examine the duration of benefits, the role of various components, mediating and moderating responses, and the ability of included interventions to demonstrate “socio-cultural relevance.”

Comparable Effectiveness

Where data was available, the Internet interventions reviewed demonstrated similar effect sizes to face-to-face interventions using comparable curriculums. The youth Internet intervention studies yielded a mean between group effect size of 0.2533, 3537, 40 with the Van Voorhees study exhibiting an NNT value of 5.26.37 Among face-to-face interventions for adolescents and emerging adults, Clarke and Garber both showed an overall mean effect size of 0.16 and NNT of 5.13 and 8.85 respectively.5556 For adult Internet intervention studies, the mean between group effect sizes at the last follow-up evaluation was 0.284143, 4546, 48, 50, 54 with the NNT values in two of the studies calculated to be 30.3 for Patten and 4.26 for Meyer.4849 Two adult face-to-face studies, focused on postpartum depression, produced a mean between group effect size of 0.60 and had a number needed to treat of 5.0 for Elliott57, 6.25 for Zlotnick58, and 6.66 Lara 59

Duration of Benefits

Most of the studies continued with a follow-up visit ranging from 2 months to a year later with enduring reductions in depressed mood. When interpreting the effectiveness of Internet interventions, the positive outcomes seen directly after treatment should also be observed at the subsequent follow-up visits. In a study of older adults, the pre/post effect size of the Internet intervention was calculated to be 1.00 immediately post-intervention.5354 Fifty-seven percent of the Internet CBT group completed the one year follow-up, and the pre/post effect size increased to 1.20, providing evidence of long-term effectiveness.54 In adult studies of Colour Your Life, MoodGym, and CATCH-IT, depression scores continued to decline at each long-term follow-up.3536, 46, 60 These results are consistent with Clarke’s findings of sustained reduction in depressed mood up to one year of follow-up, but whether mood rebounds after one year cannot be known55, 61

Moderators and Mediators

Moderators and mediators of effect have rarely been identified in the studies of focus. Higher levels of depressed mood and a greater number of prior episodes may predict poorer outcomes at follow-up, but this has not been consistently found.45, 50, 60 In the MoodGym, those who completed a greater number of modules demonstrated larger changes in depressed mood than those who did not, but the relationship between adherence and outcome has been less clear in other studies.45, 6263 Combining Internet-based approaches with other interventions may potentially enhance efficacy. For example, the combination of a motivational interview with an Internet site demonstrated an advantage in preventing depressive episodes.38 Similarly, Andersson reported that CBT combined with a discussion group demonstrated a greater effect size than CBT alone.41, 64 In a meta-analysis, guided interventions, CBT-based interventions, and experiencing major or clinical depression were associated with greater effectiveness.63

Socio-cultural Relevance

In all but one mixed gender experiment, a majority of the participants were females. Of the articles that listed gender as part of their demographics, seven contained ratios of females over 60% of the study population.36, 41, 43, 45, 49, 5354, 64 One possible reason for this is that females, in general, account for a majority of the population suffering from depression, so recruitment appears to show a bias toward women. Three studies that provided ethnicity indicated that, overall, approximately 61% of the participants were Caucasian and 25% African American.3738, 65 Also, seven adult studies indicated mostly mid to high levels of education41, 43, 4546, 49, 5354, 64, and most of the youth population was recruited from moderate to high income families33, 3538, 65. The studies spanned over many regions and to an extent reflected national cultures in Canada49, the United States3738, 45, 65, Australia33, 3536, 43, and Europe 34, 41, 46, 5254, 64 (mainly the Netherlands34, 41, 46, 5354, 64 and one in Sweden52). However, the degree to which each intervention was tailored to meet the needs of ethnic minorities or cultural differences within each country was not clearly specified. CATCH-IT included stories intended to reflect the experiences of adolescents in varying social and cultural frameworks.3738

Tailoring of interventions to personal profiles (ethnicity, form of motivation, attitudes) is supported both by theory and substantial empirical evidence. From the perspective of cognitive psychology, attention is increased with material relevant to either a “current concern” or if presented by a person with a similar life situation.14, 66 Several studies have compared tailored and standard depression and CBT-based interventions in ethnic minority populations.14 Matching of intervention to motivational style, or providing external motivation (e.g. incentives) for those who prefer external motivations and internal motivations (e.g. linking behaviors to personal goals) for those who prefer internal motivations, may enhance adherence to health and diet recommendations in public health messages.67 The PEN-3 (Persons, Environment, and Neighborhood) Model of Health Behaviors has been used to successfully adapt behavior change interventions to meet the needs of diverse populations.68 Rule-based or even an artificial intelligence model could be developed to facilitate tailoring. 69

Framework for Motivation

Like vaccines that have “adjuvant” to enhance immune response, behavior change interventions often have a motivational framework to boost response to behavior prescription (“motivation”, “positive relationships” (professional guidance and peer to peer) and an appropriate “dose” (e.g. adherence)).

Degree of Professional Guidance

In most trials, support was provided to the users throughout the online interventions, but the methods varied considerably. Additional support serves several different purposes: 1) monitors user progress on the site; 2) provides weekly lessons; and 3) determines if the users’ emotional safety is maintained.60 Support can be provided through email34, 41, 52, by phone3738, 43, 65, or no support can be given4546, 5354. In three MoodGYM studies, support was given in the school environment by a supervising professional (teacher).33, 3536 In Color Your Life, supervision was provided by a trained coach.46 One article specified that users received therapist support, but it did not define the manner in which it was provided.64

Peer-to-peer supports

Internet-based social groups (ISG) support offers the prospect of enhancing curricular-based interventions by one of several mechanisms: 1) increasing use of the curricular based prevention; 2) providing opportunities to discuss contents and increase socio-cultural relevance; and 3) directing action of social support.70 The experience of ReachOut in Australia suggests that sites that incorporate peer-based social support have considerable appeal to the public and may perhaps increase use of other more traditional learning venues.71 Online groups often sustain individual membership from for as long as 12 months (72.6% retention) and some participants experience resolution of depression symptoms after one year of participation (33.8%).72 Likewise, in a study of five online one-on-one male-to-female or female-to-female chat sessions, Shaw found some evidence that participation in Internet-based peer support groups may reduce depressed mood.7273 However, Takahashi suggested that ISGs could have negative effects on a person’s depressive tendencies, due to the influence of participants with depressive symptoms.74 With regard to enhancing social support, factors such as subjective view of social support system, belongingness, and self-esteem may improve with ISG participation. 73


Completion rates, and even the definition of completion, varied across interventions. Overall, half of the guided study participants completed about half the modules, while only about one-fifth to one-tenth of those who had unguided interventions met this mark. Participation in intervention conditions, particularly those including psychotherapy, increased attrition. For example, during the Colour Your Life trial, participants completed a mean of only 3.7 of 9 sessions.46 In a Swedish study, psychotherapy-armed participants experienced more withdrawals than the control group (psychotherapy= 37%, control= 18%).41 When MoodGYM (self-directed psychotherapy) and BluePages (psychoeducation) were compared, drop rates were greater for the psychoherapy group (MoodGYM: 25.27%, BluePages: 15.15%).43 47 A structured setting with phone follow-up may reduce dropouts. Phone calls can be for motivation, safety assessments, or education purposes.60 For example, in the primary care CATCH-IT study, only 7% dropped out at 3 months when phone follow-up was added60, while 43% dropped out in an earlier study without phone follow-up.65 Similarly, in the Colour Your Life trial, the authors reported receiving numerous contacts from their participants and provided regular guidance, and reported only a 5% drop out rate. Further, at the 2- and 3-month follow-ups, the authors reported 95% participation in the intervention group and 91% at the 6-month follow-up.46 In terms of intervention adherence (modules completed, time on site), baseline factors (younger age, higher education, higher illness severity, favorable attitudes toward the intervention), implementation factors (any structured setting), and intentional use of motivational approaches (referral by mental health specialist, use of brochures, or motivational interview by their primary care physician) may influence degree of participation in Internet Interventions.3536, 7577

Implementation Structure

A proper behavioral vaccine requires a well-thought-out implementation structure, which includes website design, varied teaching methods, and effective training.

Delivery mechanisms

Behavior change sites must balance education, behavior change, and entertainment functions in order to retain the audience, using approaches such as instructional design.78 Most of the sites providing information primarily in text format were adult intervention studies41, 43, 4546, 49, 5354, 64, and most were tested on individuals with moderate to high levels of education.41, 43, 4546, 49, 5354, 64 Supplying fewer interactive exercises and more textual information could possibly yield lower participation rates due to decreased participation, which could cause lower effect sizes.44 Conversely, the use of a range of media experiences including, games, music, video, stories, photography, virtual reality, and in-the-moment cell phone interactions may substantially enhance the intrinsic appeal of sites (e.g., ReachOut71 and YooMagazine79). However, we must consider the technical infrastructure available in developing countries, which may not support broadband applications.

Delivery Context

The depression interventions and Internet sites were provided at several locations, depending on the study. Three of the interventions, all MoodGYM, were offered in a school setting33, 3536 while the other interventions were accessed at the users' homes,34, 3738, 4042, 4550, 5254, 65, which required home Internet access to participate in the research. However, the main form of variation between most of these studies existed in the form and location of the initial interview and assessment. Five studies conducted the first interview and assessment within a primary care facility3738, 46, 5354, 65, three in a school environment33, 3536, three by Internet and e-mail41, 52, 64, and three by brochures.34, 45 Two assessment strategies producing the best participation were Internet and primary care. In a study of a CBT intervention program with an initial Internet assessment, all participants reached 4 of the 5 modules.41, 40 In the Clarke HMO studies40, 45, recruitment via search of medical records was used, which may herald future integrative technologies. Integrative technologies such as rule-based algorithms or even artificial intelligence may be able to link interventions to medical records as well as various delivery platforms such as cell phones.


One challenge to Internet-based depression prevention is the risk of self-harm or progression to major depression among those who are at risk by virtue of sub-threshold depressed mood.60, 80 Specifically, the participant may not be observed for extended period or have meaningful interaction with a experienced care provider. Under these circumstances, the underlying depressive illness can progress as well as self-harm intent. Three studies reported that after becoming aware of such depression scores, the participants were recommended to consult their physician.34, 5254 Increased monitoring was another method used including “checking up” on their participants via e-mail, phone, or direct contact to discuss progress and/or concerns.34, 3738, 43, 49, 52 However, most articles either did not assess self-harm risk and/or did not report additional safety precautions. A comprehensive, standard approach to safety management approaches does not appear to have been developed.


Considerable progress has been made in developing prototype “behavioral vaccines” for depressive disorders across the life course that include effective components, frameworks for motivation, and a structured implementation strategy. The greatest number of interventions has been developed for middle-aged adults and, to a lesser extent, for adolescents and emerging adults, while no interventions have been developed and published for children or post-partum depression. With regard to effective intervention components, there is substantial evidence that CBT-based interventions can achieve comparable results to face-to-face interventions with benefits sustained one year following treatment. However, the vast majority of studies were not formal prevention studies, so we cannot fully know if components deployed in "treatment" studies would be as effective in prevention. Also, most studies enrolled primarily educated populations of “European ethnic decent”; there is a dearth of research on Internet interventions with ethnically diverse populations. Internet support or peer-to-peer contact offers the promise of both providing a “draw” to the interventions while also potentiating the effects of curricular program elements. As with vaccines, schools and primary care may be auspicious environments to implement these interventions and such settings may engender higher levels of uptake of “behavioral vaccines.” In the future, artificial intelligence-driven risk monitoring in schools, work places, and primary care could deploy personalized “behavioral vaccines” to alter illness trajectories and consequently reduce morbidity while enhancing positive development.

Future Directions

Interventions over the life course

In order to determine if current interventions are effective in the prevention of depression, formal study designs are needed with structured psychiatric interviews and longitudinal data analysis to examine survival curves (dichotomous outcomes).1819 Appropriate comparison groups need to be determined with reference to research ethics and the de facto mental health care system.81 "Behavioral vaccines" need to be compared to effective face-to-face preventive strategies that are often unavailable, too complex, and too expensive for general distribution. We need to broaden assessed outcomes to include achievement of developmental milestones in youth and measures of well-being, resiliency, productivity, and functional status in adults to enhance relevance to policy makers. Furthermore, the cost effectiveness of such programs needs to be addressed, and the optimal schedule for each intervention, and booster sessions within each intervention, needs to be defined.

Effective Intervention Components

For “behavioral vaccines” to develop, we need to identify the key effective content and delivery mechanisms. In addition to dismantling studies, formal moderator and mediator analyses and factorial study designs could facilitate this goal. Methods to inexpensively adapt standard interventions to specific cultural settings worldwide need to be determined. Specifically, we must determine which elements can be shared across large cultural areas and which must be adapted locally. Combining interventions with other modalities, with differing mechanisms of action such as nutritional supplements (e.g. essential fatty acids) and exercise, could increase the efficacy of interventions.8283

Framework for Motivation

Current interventions may lack sufficient appeal to attain their public health value because relatively few people are “ready” and willing to participate in such self-directed curricular programs.84 Combinations of education, behavior change, and entertainment need to be created (REACH OUT71 and YooMagazine79), and/or social media experiences need to be developed, such as peer-to-peer Internet support groups.41, 7274

Implementation Structure

It is essential that implementation models be developed that allow these interventions to be effectively delivered to defined populations whereby full public health impact can be assessed.85 More engaging delivery mechanisms would integrate multiple elements together, including peer-to-peer support, games, music, video, advanced technology platforms (e.g., virtual reality experiences), cell phone-based guidance and feedback, and integrative technologies (e.g., rule based programs and true artificial intelligence).


Supported by a career development award from the National Institutes of Mental Health (NIMH K-08 MH 072918-01A2)


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Disclosures: Benjamin W. Van Voorhees has served as a consultant to Prevail Health Solutions, Inc, Mevident Inc, San Francisco and Social Kinetics, Palo Alto, CA, and the Hong Kong University to develop Internet-based interventions. In order to facilitate dissemination, the University of Chicago recently agreed to grant a no-cost license to Mevident Incorporated (3/5/2010) to develop a school-based version. Neither Dr. Van Voorhees nor the university will receive any royalties or equity. Dr. Van Voorhees has agreed to assist the company in adapting the intervention at the rate of $1,000/day for 5.5 days. The CATCH-IT Internet site and all materials remain open for public use and made freely available to healthcare providers at http://catchit-public.bsd.uchicago.edu/.


1. World Health Organization. Conquering depression. New Dehli: World Health Organization; 2001. Regional office for South-East Asia.
2. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005 Jun;62(6):593–602. [PubMed]
3. Hankin BL. Adolescent depression: description, causes, and interventions. Epilepsy Behav. 2006 Feb;8(1):102–114. [PubMed]
4. Kessler RC, Berglund P, Demler O, et al. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R) Journal of the American Medical Association. 2003 Jun 18;289(23):3095–3105. [PubMed]
5. DeRubeis RJ, Hollon SD, Amsterdam JD, et al. Cognitive therapy vs medications in the treatment of moderate to severe depression. Archives of General Psychiatry. 2005 Apr;62(4):409–416. [PubMed]
6. Warden D, Rush AJ, Trivedi MH, Fava M, Wisniewski SR. The STAR*D Project results: a comprehensive review of findings. Curr Psychiatry Rep. 2007 Dec;9(6):449–459. [PubMed]
7. Lewinsohn PM, Rohde P, Seeley JR, Klein DN, Gotlib I. The consequences of adolescent major depressive disorder on young adults. In: Joiner TE, Brown JS, Kistner J, editors. The interpersonal, cognitive and social nature of depression. Mahwah, NJ: Lawrence Erlbaum Associates; 2006. pp. 43–68.
8. Rohde P, Lewinsohn PM, Seeley JR. Are adolescents changed by an episode of major depression? Journal of the American Academy of Child and Adoelscent Psychiatry. 1994;33:1289–1298. [PubMed]
9. Weissman MM, Wolk S, Goldstein RB, et al. Depressed adolescents grown up. Journal of the American Medical Association. 1999;17:7–13. [PubMed]
10. Greenberg PE, Kessler RC, Birnbaum HG, et al. The economic burden of depression in the United States: how did it change between 1990 and 2000? J Clin Psychiatry. 2003 Dec;64(12):1465–1475. [PubMed]
11. Eisen J, Van Voorhees B. Pilot Study of Implementation of an Internet-Based Depression Prevention Intervention (CATCH-IT) for Adolescents in 12. US Primary Care Practices. Chicago: 2010.
12. Jaycox LH, Miranda J, Meredith LS, Duan N, Benjamin B, Wells K. Impact of a primary care quality improvement intervention on use of psychotherapy for depression. Ment Health Serv Res. 2003;5(2):109–120. [PubMed]
13. Munoz RF, Cuijpers P, Smit F, Barrera AZ, Leykin Y. Prevention of major depression. Annu Rev Clin Psychol. 2010 Apr 27;6:181–212. [PubMed]
14. Van Voorhees BW, Walters AE, Prochaska M, Quinn MT. Reducing Health Disparities in Depressive Disorders Outcomes between Non-Hispanic Whites and Ethnic Minorities: A Call for Pragmatic Strategies over the Life Course. Med Care Res Rev. 2007 Aug 31; [PubMed]
15. Cuijpers P, van Straten A, Smit F, Mihalopoulos C, Beekman A. Preventing the onset of depressive disorders: a meta-analytic review of psychological interventions. American Journal of Psychiatry. 2008;165(10):1272. [PubMed]
16. Cuijpers P, Muñoz RF, Clarke GN, Lewinsohn PM. Psychoeducational treatment and prevention of depression: The “coping with depression” course thirty years later. Clinical Psychology Review. 2009;29(5):449–458. [PubMed]
17. Lewinsohn PM, Antonuccio DO, Breckenridge JS, Teri L. The ‘Coping with Depression’ course. Eugene: Castalia Publishing Company; 1984.
18. Mrazek PB, Haggerty RJ. Reducing risks for mental disorders: Frontiers for preventive intervention research. Washington, DC: National Academy Press; 1994.
19. National Research Council and Institute of Medicine. Preventing Mental, Emotional, and Behavioral Disorders Among Young People: Progress and Possibilities. Washington, DC: The National Academies Press; 2009.
20. Van Voorhees BW, Watson N, Bridges J. Development and pilot study of a marketing strategy for primary care/internet based depression prevention intervention for adolescents (CATCH-IT) Journal of Clinical Psychiatry Primary Care Companion. in press. [PMC free article] [PubMed]
21. Crutzen R, de Nooijer J, Brouwer W, Oenema A, Brug J, de Vries NK. A conceptual framework for understanding and improving adolescents' exposure to Internet-delivered interventions. Health Promot Int. 2009 Sep;24(3):277–284. [PubMed]
22. De Los Reyes A, Kazdin AE. Conceptualizing changes in behavior in intervention research: the range of possible changes model. Psychol Rev. 2006 Jul;113(3):554–583. [PMC free article] [PubMed]
23. Ritterband LM, Thorndike FP, Cox DJ, Kovatchev BP, Gonder-Frederick LA. A behavior change model for internet interventions. Ann Behav Med. 2009 Aug;38(1):18–27. [PMC free article] [PubMed]
24. Van Voorhees B, Fogel J, Pomper B, R D. Adolescent Dose and Ratings of an Internet-Based Depression Prevention Program: A Randomized Trial of Primary Care Physician Brief Advice versus a Motivational Interview. ournal of Cognitive and Behavioral Psychotherapies. 2009;9(1) [PMC free article] [PubMed]
25. Gray NJ, Klein JD, Cantrill JA, Noyce PR. Adolescent girls' use of the Internet for health information: issues beyond access. J Med Syst. 2002;26(6):545–553. [PubMed]
26. Nation M, Crusto C, Wandersman A, et al. What works in prevention. Principles of effective prevention programs. Am Psychol. 2003;58(6–7):449–456. [PubMed]
27. Wandersman A. Community science: bridging the gap between science and practice with community-centered models. American Journal of Community Psychology. 2003;31(3–4):227–242. [PubMed]
28. Landback J, Prochaska M, Ellis J, et al. From prototype to product: development of a primary care/internet based depression prevention intervention for adolescents (CATCH-IT) Community Ment Health J. 2009 Oct;45(5):349–354. [PubMed]
29. Van Voorhees BW, Ellis JM, Gollan JK, et al. Development and Process Evaluation of a Primary Care Internet-Based Intervention to Prevent Depression in Emerging Adults. Prim Care Companion J Clin Psychiatry. 2007;9(5):346–355. [PMC free article] [PubMed]
30. Muñoz R, Barrera A. A worldwide Internet-based CBT intervention for postpartum depression prevention: Adaptation, trial and challenges. Association for Behavioral and Cognitive Therapies. 2010
31. Fung D. Legend of the Snow Orchid. 2010. https://www.roc-n-ash.com/imheportal/welcome/ 2010,
32. Gillham JE, Reivich KJ. Prevention of depressive symptoms in school children: A research update. Psychological Science. 1999;10(5):461–462.
33. Calear AL, Christensen H, Mackinnon A, Griffiths KM, O'Kearney R. The YouthMood Project: a cluster randomized controlled trial of an online cognitive behavioral program with adolescents. J Consult Clin Psychol. 2009 Dec;77(6):1021–1032. [PubMed]
34. Hoek W, Schuurmans J, Koot HM, Cuijpers P. Prevention of depression and anxiety in adolescents: a randomized controlled trial testing the efficacy and mechanisms of Internet-based self-help problem-solving therapy. Trials. 2009;10:93. [PMC free article] [PubMed]
35. O'Kearney R, Gibson M, Christensen H, Griffiths KM. Effects of a cognitive-behavioural internet program on depression, vulnerability to depression and stigma in adolescent males: a school-based controlled trial. Cognitive behaviour therapy. 2006;35(1):43–54. [PubMed]
36. O'Kearney R, Kang K, Christensen H, Griffiths K. A controlled trial of a school-based Internet program for reducing depressive symptoms in adolescent girls. Depress Anxiety. 2009;26(1):65–72. [PubMed]
37. Van Voorhees BW, Fogel J, Reinecke MA, et al. Randomized clinical trial of an Internet-based depression prevention program for adolescents (Project CATCH-IT) in primary care: 12-week outcomes. J Dev Behav Pediatr. 2009 Feb;30(1):23–37. [PubMed]
38. Van Voorhees BW, Vanderplough-Booth K, Fogel J, et al. Integrative internet-based depression prevention for adolescents: a randomized clinical trial in primary care for vulnerability and protective factors. J Can Acad Child Adolesc Psychiatry. 2008 Nov;17(4):184–196. [PMC free article] [PubMed]
39. O'Kearney R, Gibson M, Christensen H, Griffiths KM. Effects of a cognitive-behavioural internet program on depression, vulnerability to depression and stigma in adolescent males: a school-based controlled trial. Cogn Behav Ther. 2006;35(1):43–54. [PubMed]
40. Clarke G, Kelleher C, Hornbrook M, Debar L, Dickerson J, Gullion C. Randomized effectiveness trial of an Internet, pure self-help, cognitive behavioral intervention for depressive symptoms in young adults. Cogn Behav Ther. 2009 Dec;38(4):222–234. [PMC free article] [PubMed]
41. Andersson G, Bergstrom J, Hollandare F, Carlbring P, Kaldo V, Ekselius L. Internet-based self-help for depression: randomised controlled trial. Brit J Psychiat. 2005 Nov;187:456–461. [PubMed]
42. Cavanagh K, Shapiro DA, Van Den Berg S, Swain S, Barkham M, Proudfoot J. The effectiveness of computerized cognitive behavioural therapy in routine care. Br J Clin Psychol. 2006 Nov;45(Pt 4):499–514. [PubMed]
43. Christensen H, Griffiths KM, Jorm AF. Delivering interventions for depression by using the internet: randomised controlled trial. BMJ. 2004 Jan 31;328(7434):265. [PMC free article] [PubMed]
44. Christensen H, Griffiths KM, Korten A. Web-based cognitive behavior therapy: analysis of site usage and changes in depression and anxiety scores. J Med Internet Res. 2002 Jan–Mar;4(1):e3. [PMC free article] [PubMed]
45. Clarke G, Reid E, Eubanks D, et al. Overcoming depression on the Internet (ODIN): a randomized controlled trial of an Internet depression skills intervention program. J Med Internet Res. 2002 Dec;4(3):E14. [PMC free article] [PubMed]
46. de Graaf LE, Gerhards SA, Arntz A, et al. Clinical effectiveness of online computerised cognitive-behavioural therapy without support for depression in primary care: randomised trial. Br J Psychiatry. 2009 Jul;195(1):73–80. [PubMed]
47. Mackinnon A, Griffiths KM, Christensen H. Comparative randomised trial of online cognitive-behavioural therapy and an information website for depression: 12-month outcomes. Br J Psychiatry. 2008 Feb;192(2):130–134. [PubMed]
48. Meyer B, Berger T, Caspar F, Beevers CG, Andersson G, Weiss M. Effectiveness of a novel integrative online treatment for depression (Deprexis): randomized controlled trial. J Med Internet Res. 2009;11(2):e15. [PMC free article] [PubMed]
49. Patten SB. Prevention of depressive symptoms through the use of distance technologies. Psychiatr Serv. 2003 Mar;54(3):396–398. [PubMed]
50. Proudfoot J, Goldberg D, Mann A, Everitt B, Marks I, Gray JA. Computerized, interactive, multimedia cognitive-behavioural program for anxiety and depression in general practice. Psychol Med. 2003 Feb;33(2):217–227. [PubMed]
51. Cavanagh J, Geisler MW. Mood effects on the ERP processing of emotional intensity in faces: a P3 investigation with depressed students. Int J Psychophysiol. 2006 Apr;60(1):27–33. [PubMed]
52. Warmerdam L, van Straten A, Cuijpers P. Internet-based treatment for adults with depressive symptoms: the protocol of a randomized controlled trial. BMC Psychiatry. 2007;7:72. [PMC free article] [PubMed]
53. Spek V, Nyklicek I, Smits N, et al. Internet-based cognitive behavioural therapy for subthreshold depression in people over 50 years old: a randomized controlled clinical trial. Psychol Med. 2007 Dec;37(12):1797–1806. [PubMed]
54. Spek V, Cuijpers P, Nyklicek I, et al. One-year follow-up results of a randomized controlled clinical trial on internet-based cognitive behavioural therapy for subthreshold depression in people over 50 years. Psychol Med. 2008 May;38(5):635–639. [PubMed]
55. Clarke GN, Hornbrook M, Lynch F, et al. A randomized trial of a group cognitive intervention for preventing depression in adolescent offspring of depressed parents. Arch Gen Psychiatry. 2001 Dec;58(12):1127–1134. [PubMed]
56. Garber J, Clarke GN, Weersing VR, et al. Prevention of depression in at-risk adolescents: a randomized controlled trial. JAMA. 2009 Jun 3;301(21):2215–2224. [PMC free article] [PubMed]
57. Elliott SA, Leverton TJ, Sanjack M, et al. Promoting mental health after childbirth: a controlled trial of primary prevention of postnatal depression. Br J Clin Psychol. 2000 Sep;39(Pt 3):223–241. [PubMed]
58. Zlotnick C, Miller IW, Pearlstein T, Howard M, Sweeney P. A preventive intervention for pregnant women on public assistance at risk for postpartum depression. Am J Psychiatry. 2006 Aug;163(8):1443–1445. [PubMed]
59. Lara MA, Navarro C, Navarrete L. Outcome results of a psycho-educational intervention in pregnancy to prevent PPD: a randomized control trial. J Affect Disord. 2010 Apr;122(1–2):109–117. [PubMed]
60. Van Voorhees BW, Fogel J, Reinecke MA, et al. Randomized Clinical Trial of an Internet-Based Depression Prevention Program for Adolescents (Project CATCH-IT) in Primary Care: 12-Week Outcomes. J Dev Behav Pediatr. 2008 Feb 3; [PubMed]
61. Clarke GN, Hawkins W, Murphy M, Sheeber LB, Lewinsohn PM, Seeley JR. Targeted prevention of unipolar depressive disorder in an at-risk sample of high school adolescents: a randomized trial of a group cognitive intervention. Journal of American Academic Child and Adolescent Psychiatry. 1995 Mar;34(3):312–321. [PubMed]
62. Christensen H, Griffiths K, Groves C, Korten A. Free range users and one hit wonders: community users of an Internet-based cognitive behaviour therapy program. Aust N Z J Psychiatry. 2006 Jan;40(1):59–62. [PubMed]
63. Gellatly J, Bower P, Hennessy S, Richards D, Gilbody S, Lovell K. What makes self-help interventions effective in the management of depressive symptoms? Meta-analysis and meta-regression. Psychol Med. 2007 Sep;37(9):1217–1228. [PubMed]
64. Andersson G, Bergstrom J, Hollandare F, Ekselius L, Carlbring P. Delivering cognitive behavioural therapy for mild to moderate depression via the Internet: Predicting outcome at 6-month follow-up. Verhaltenstherapie. 2004;14(3):185–189.
65. Van Voorhees BW, Ellis J, Stuart S, Fogel J, Ford DE. Pilot study of a primary care internet-based depression prevention intervention for late adolescents. Can Child Adolesc Psychiatr Rev. 2005 May;14(2):40–43. [PMC free article] [PubMed]
66. Landsback J, Van Voorhees B. From Prototype to Product: Development of a Primary Care/Internet Based Depression Prevention Intervention for Adolescents (CATCH-IT) The Community Mental Health Journal. in press. [PubMed]
67. Resnicow K, Davis RE, Zhang G, et al. Tailoring a fruit and vegetable intervention on novel motivational constructs: results of a randomized study. Ann Behav Med. 2008 Apr;35(2):159–169. [PubMed]
68. Matthews AK, Sanchez-Johnsen L, King A. Development of a Culturally Targeted Smoking Cessation Intervention for African American Smokers. J Community Health. 2009 Aug 29; [PMC free article] [PubMed]
69. John R, Buschman P, Chaszar M, Honig J, Mendonca E, Bakken S. Development and evaluation of a PDA-based decision support system for pediatric depression screening. Stud Health Technol Inform. 2007;129(Pt 2):1382–1386. [PubMed]
70. Griffiths KM, Crisp D, Christensen H, Mackinnon AJ, Bennett K. The ANU WellBeing study: a protocol for a quasi-factorial randomised controlled trial of the effectiveness of an Internet support group and an automated Internet intervention for depression. BMC Psychiatry. 2010;10:20. [PMC free article] [PubMed]
71. Coyle D, Doherty G, Sharry J. An evaluation of a solution focused computer game in adolescent interventions. Clin Child Psychol Psychiatry. 2009 Jul;14(3):345–360. [PubMed]
72. Houston TK, Cooper LA, Ford DE. Internet support groups for depression: a 1-year prospective cohort study. Am J Psychiatry. 2002 Dec;159(12):2062–2068. [PubMed]
73. Shaw LH, Gant LM. In Defense of the internet: The relationship between Internet communication and depression, loneliness, self-esteem, and perceived social support. Cyberpsychology & Behavior. 2002 Apr;5(2):157–171. [PubMed]
74. Takahashi Y, Uchida C, Miyaki K, Sakai M, Shimbo T, Nakayama T. Potential benefits and harms of a peer support social network service on the internet for people with depressive tendencies: qualitative content analysis and social network analysis. J Med Internet Res. 2009;11(3):e29. [PMC free article] [PubMed]
75. Batterham PJ, Neil AL, Bennett K, Griffiths KM, Christensen H. Predictors of adherence among community users of a cognitive behavior therapy website. Patient Prefer Adherence. 2008;2:97–105. [PMC free article] [PubMed]
76. Marko M, Fogel J, Mykerezi E, Van Voorhees B. Adolescent Internet Depression Prevention: Preferences for Intervention and Predictors of Intentions and Adherence. Journal of Psyberpsychology and Rehabilitation. in press.
77. Neil AL, Batterham P, Christensen H, Bennett K, Griffiths KM. Predictors of adherence by adolescents to a cognitive behavior therapy website in school and community-based settings. J Med Internet Res. 2009;11(1):e6. [PMC free article] [PubMed]
78. Gagne RMBL, Wager WW. Principles of Instructional Design. Fort Worth, TX: Harcourt Brace Jovanovich College Publishers; 1992.
79. Santor DA, Poulin C, LeBlanc JC, Kusumakar V. Online health promotion, early identification of difficulties, and help seeking in young people. J Am Acad Child Adolesc Psychiatry. 2007 Jan;46(1):50–59. [PubMed]
80. de Graaf LE, Huibers MJ, Cuijpers P, Arntz A. Minor and major depression in the general population: does dysfunctional thinking play a role? Compr Psychiatry. 2010 May–Jun;51(3):266–274. [PubMed]
81. Regier DA, Narrow WE, Rae DS, Manderscheid RW, Locke BZ, Goodwin FK. The de facto US mental and addictive disorders service system. Epidemiologic catchment area prospective 1-year prevalence rates of disorders and services. Arch Gen Psychiatry. 1993 Feb;50(2):85–94. [PubMed]
82. Cuijpers P. Review: exercise may moderately improve depressive symptoms. Evid Based Ment Health. 2009 Aug;12(3):76–77. [PubMed]
83. Lakhan SE, Vieira KF. Nutritional therapies for mental disorders. Nutr J. 2008;7:2. [PMC free article] [PubMed]
84. Van Voorhees B, Watson N, Bridges J, et al. Development and Pilot Study of a Marketing Strategy for Primary Care/Internet Based Depression Prevention Intervention for Adolescents (CATCH-IT) Journal of Clinical Psychiatry Primary Care Companion. in press. [PMC free article] [PubMed]
85. Glasgow RE, Vogt TM, Boles SM. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health. 1999 Sep;89(9):1322–1327. [PMC free article] [PubMed]
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