Mental health disorders (MHD) are highly prevalent, with estimated lifetime and 12 month-prevalence rates, ranging across countries between 18.1–36.1 and 9.8–19.1%, respectively (1). MHD are one of the leading causes of disability (2) and associated with an immense disease burden such as poorer quality of life of sufferers and their loved ones, an increased risk of developing chronic physical conditions and related mortality (3–5). The economic burden of these disorders is enormous, including substantial economic costs, reduced workforce participation, occupational impairment, and lost productivity (6–8).

In the past decades, a variety of interventions have been developed to treat MHD for which efficacy has been demonstrated in a large number of randomized trials (9, 10). However, even assuming the hypothetical scenario of 100% coverage and compliance to evidence-based treatments, approximately only 28% of the disease burden attributable to MHD could be averted (11). In fact, less than half of the individuals with a MHD are recognized and treated (12). Therefore, attention has increasingly been focused on the prevention of MHD.

Preventive interventions can be classified as universal interventions, directed at the whole population; selective interventions, directed at individuals with specific risk factors for the development of a MHD; or as indicated preventive interventions, directed at individuals in the prodromal stage of a disorder, who do not yet fulfill the criteria for a full blown disorder but experience subclinical symptoms (13).

Emerging evidence indicates the potential of psychological interventions for the prevention of MHD. For example, in a recent meta-analysis, van Zoonen and colleagues found psychological interventions aiming to prevent major depressive disorders (MDDs) to reduce the incidence by approximately 22% (14). Results from another review found that cognitive behavioral indicated preventive interventions reduced the transition to psychosis with a risk ratio of 0.54 (95%-CI: 0.34–0.86) (15); encouraging evidence from a limited number of randomized controlled trials is also available, for example, for the prevention of eating disorders (EDs) (16) and tobacco use (17), whereas the efficacy for other disorders such as interventions to prevent anxiety (18) is not yet established.

Although psychological interventions might have a tremendous potential for the prevention of MHD, their current impact on the reduction of disease burden is questionable. Possible reasons include that it is not practical to deliver those interventions to the community en masse due to limited health care resources and the limited availability of evidence-based interventions and clinicians in routine practice, especially in rural areas. Therefore, new approaches are needed to maximize the impact of psychological preventive interventions.

Limitations of traditional prevention programs could potentially be overcome by providing Internet- and mobile-based interventions (IMIs). This relatively new medium for promoting mental health and preventing MHD introduces a fresh array of possibilities, including the provision of evidence-based psychological interventions that are free from the restraints of travel and time and allow reaching participants for whom traditional opportunities are not an option.

Internet- and mobile-based interventions have been shown to be effective in clinical populations, including the treatment of depression (19–21), anxiety (20, 22, 23), alcohol use (24), and sleep disorders (25). However, evidence for their effectiveness in preventing the incidence of MHD is much less documented.

This article provides an introduction to the subject and narratively reviews the available evidence for the effectiveness of IMIs with regard to the prevention of MHD onsets. Subsequently, we will offer some suggestions regarding the direction of future research in this field.

The possibilities to use IMIs for the prevention of MHD range from mobile-based apps for the monitoring of health behavior and stand-alone self-help interventions to supplemental elements integrated in conventional on-site psychological interventions (blended concepts). One common element of such interventions is that emotional, cognitive, and behavioral processes are modified and their generalizations to users’ daily lives promoted using established psychological techniques (26). IMIs can be categorized in regard to their use of technology, the extent of human support, the theoretical basis, and with respect to their areas of applications and indications (Figure 1).

While there are currently well over 100 randomized controlled studies on Internet- and mobile-based concepts fostering mental health, only few studies to date have investigated their potential in preventing the incidence or onset of a MHD. In the following section, we will review these studies and thereby only focus on studies that assessed the effects of an IMI on MHD onset (assessed according to diagnostic and statistical manual of mental disorders/ICD criteria) in a sample of adults, adolescents, or children, who is free from an acute mental disorder at baseline. There are further studies on mental health IMIs evaluated in samples with subthreshold mental health conditions. However, these studies were often planned for other purposes than examining disorder onset and limited in their methodological quality (47). Table 1 gives an overview of the reviewed studies, and Tables 2 and 3 are about the characteristics of the reviewed studies and interventions.

Although significant strides have been made in recent years regarding the development of effective IMIs for the prevention of MHD, there are several important directions for future research.

First, the number of randomized controlled trials that have been conducted to date is very limited and so far it is not possible to draw definite conclusions about the potential of IMIs for the prevention of MHD for specific disorders. Only for the indicated prevention of depression there is consistent evidence across four different trials (three primary prevention trials aiming to reduce first incidence, one relapse prevention trial). However, only one primary prevention trial (48) and the relapse prevention trial (56) used standard diagnostic procedures, while the other two trials relied only on self-report questionnaires for onset identification. The only trial on the prevention of general anxiety did not result in positive findings. In terms of EDs, effects were only found in post hoc subgroup analyses, indicating that it might be possible to prevent ED onset for subpopulations of people at risk of developing EDs. Future studies need to identify those subpopulations likely to profit from preventive IMIs. It should be noted that two of the five successful prevention trials with positive findings (54, 56) were based on very small sample sizes, whereas several much larger trials did not find positive results. Disorders not examined so far include substance use disorders, bipolar disorders, stress-related disorders, phobic disorders and panic disorder, obsessive–compulsive disorder, impulse-control disorders, somatic symptom disorder and insomnia. However, it is of note that there is quite substantial evidence for the effectiveness of health behavior change IMIs regarding the reduction of problematic alcohol consumption (24), improving sleep (66–68), reducing work-related stress (36, 69–71), all of which might be useful as MHD prevention IMIs as well. In summary, there is a need for more rigorously conducted large-scale randomized controlled trials using standard clinical diagnostic instruments for the selection of participants without MHD at baseline and the assessment of MHD onset.

Second, one general problem of prevention trials is that one needs very large sample sizes in order to be able to detect existing differences between groups, as transition rates to full blown disorders tend to be low during follow-ups of typical length of controlled studies even in high-risk groups. This problem is (could be) true of many of the studies reviewed above (49–51, 53, 54, 58). Given the low chance of positive findings in a trial that is not primarily designed nor powered to detect such findings, it is understandable that many prevention researchers abstain from assessing diagnostic status. However, even without positive findings such information would be very valuable for the field and we would like to encourage researchers to assess these data. Diagnostic status data could help to obtain the necessary information on the transition rates of the target population and generate hypotheses on the size of the effect on MHD onset, which are both necessary to design and power subsequent trials adequately. Moreover, if several of such smaller trials would be conducted, the data of these trials could be combined using individual participant data meta-analytic techniques (72, 73). By collecting and pooling the primary data of individual trials, multiple underpowered trials can contribute to a large enough pooled sample size with sufficient power to examine effects on the incidence of MHD as well as analyzing patient subgroups and other effect modifying variables (74). If conducting observer-based clinical interviews is too expensive, web-based self-administered version of instruments such as the WHO-CIDI could be a low-cost alternative (75).

Provided that preventive IMIs are transferable to clinical praxis, we further need to establish the best way to implement preventive IMIs into our health care systems. Integrating IMIs as a first step of stepped-care approaches might be one promising way. Prevention IMIs for MHDs might also easily be integrated in already existing prevention programs (blended MHD prevention). That this would be worthwhile is supported by recent meta-analytic findings, indicating that traditional FTF interventions profit from providing additional IMI-components (76). Lindhiem and colleagues showed in their systematic review on 10 RCTs that a mobile component as a supplemental element in psychological interventions (e.g., SMS to support behavior changes between therapy sessions) considerably increase the effectiveness of these interventions compared to the respective strictly on-site interventions (SMD = 0.27) (76). IMIs also offer the unique potential of reaching people who would not access preventive mental health care via the established channels delivery, either because they do not utilize the available offers or because they do not feel comfortable discussing their mental health issues with their general practitioners and mental health specialists. Thus, we should also think about implementing preventive MHD–IMIs in a way that allows people to self-refer to IMIs [10]. This might, for example, be achievable by providing preventive IMIs via websites of established associations or as a direct prevention offer from health insurance companies, which would probably make it necessary for several countries to think about alternative financing health care models to integrate preventive IMIs in the best possible way. However, an approach just aimed at the GP and health insurance companies might be too narrow to exploit the potential of preventive services, as the majority of the target population might not use them. Preventive IMIs should be, therefore, I think that preventive interventions should be delivered through multiple channels that have “natural” possibilities to engage people, such as schools (they can reach all students), universities and colleges, pregnant women (because they all receive prenatal care), patients with general medical disorders, but also, for example, companies.

Most of the trials that have been conducted up to this point have included only adults as participants. Although the general potential of IMIs to foster mental health in children and adolescents has been documented (20), only one study has investigated the effects of a prevention IMI, delivered as a universal preventive approach, on MHD onset (52). Given that ~75% of all MHD have their onset before the age of 25 (77), future studies should explore the potential of IMIs for preventing the first incidence in children, adolescents and young adults. That this is possible using a psychological intervention has been shown, for example, for depression (78). An interesting development in this field is a shift away from traditional computerized and browser-based interventions to mobile-based smart phones interventions. Using the most up-to-date technology and/or access paths for providing mental health prevention might increase the interventions attractiveness and user-friendliness in this modern technology oriented age group. However, evidence from mobile-based interventions and other mode of deliveries is yet scarce and future studies are needed to explore their potential. One of these other possibilities are serious games or even augmented reality interventions, that go beyond what can be done in typically “talking” interventions likely to attract children and adolescents. The potential of serious games to foster mental health has been shown, for example, in the field of depression (29, 79), but yet there is no evidence regarding the prevention of MHD onset, with one trial currently being conducted (80). The use of augmented reality has to the best of our knowledge not yet been explored. One general problem with psychological interventions for the prevention of MHD in children and adolescents is that one needs parental consent, which is difficult to obtain online in a reliable manner, and which can be seen as barrier to reach these high-risk groups.

Although many MHD already have their initial incidence before college matriculation (81), college entry might nevertheless be a very promising point in time to deliver preventive psychological interventions. College entry allows screening of the whole college student population, identification of those at risk for development of mental health problems, and subsequent offers of targeted preventive interventions. These might be disorder-specific prevention IMIs or trainings focusing on missing skills and competencies (e.g., procrastination, limited social competencies, and low self-efficacy) as well as other known risk factors for developing mental disorders. Similarly, entering vocational schools and the working environment might be a possibility to establish mental health screening and subsequent mental health trainings. In this context, where freshmen might be anxious about disclosing mental health issues, the possibility of IMIs being provided anonymously can be regarded as one substantial advantage over other occupational and college mental health management programs.

After development of an IMI, ongoing costs are directly related to guidance time. Hence, evaluating and comparing the effectiveness of interventions with different guidance formats is of particular importance. In the present review, only three of ten trials examined an unguided intervention, of which only one found significant, but very small effects on disorder onset. This is in line with previous findings from the clinical field that indicate that IMIs with human support have a significantly greater success than IMIs without therapeutic support (82–84). However, unguided IMIs might still produce larger effects at population level with regard to the reduction of disease burden, as more individuals could be reached at a given budget (85). A recent review on the cost-effectiveness of IMIs for improving mental health problems suggested that guided IMIs might be more cost-effective than unguided IMIs despite their higher intervention costs per participants (86). Hence, there is a need for studies that compare not only the cost-effectiveness of guidance vs. no guidance in randomized trials but also different intensities and forms of guidance [e.g., guidance concept with individual feedback on the completed exercises, i.e., content feedback, vs. feedback aiming only to increase the adherence to the intervention (37)]. Moreover, the type of human support might not only have an impact on the effectiveness of interventions but also on the willingness to use such interventions. Given that the effects of interventions on a population level also depend on the acceptance and the reach of the target population, studies should address both effectiveness and reach of IMIs with different forms of guidance.

Implementing preventive IMIs into our health care system might be a promising strategy regarding the cost-effectiveness of the health care system as has been recently estimated on the basis of a Markov-model study (87). However, evidence from randomized controlled trials is still scarce when it comes to IMIs for the prevention of MHD Studies indicated that guided Internet interventions for depression, anxiety, sleeping problems, smoking cessation, and alcohol consumption have favorable probabilities of being more cost-effective when compared to controls (86, 88). However, these studies were mainly directed at the treatment of mental health problems, and only one study has to the best of our knowledge been published so far, that investigated the cost-effectiveness of an IMI with regard to the prevention of MHD onset (59). Two ongoing studies might provide first results in the near future (41, 46).

As with any other method, it is important to take into account the limitations and risks are involved with IMIs alongside all of the potential benefits of the procedure. At this stage, however, reliable empirical information showing negative effects of preventive IMIs has been very limited (89) and also in the present review, none of the identified studies reported results on potential negative effects.

Potential risks and negative effects include, depending on the concept, the following points, among others: (1) limited ability to timely identify patients prone to self-injury, for example, in relapse prevention interventions; (2) the development of reduced health-related self-efficacy if participants are not successful with a stand-alone IMI; and (3) the development of negative attitudes of non-responders toward psychological interventions in general and as a result a reduced willingness to utilize mental health care in case of MHD onset. Possible negative effects of such interventions cannot be ruled out at present, which counts to a similar degree also to classical FTF psychological interventions. There is an urgent need for further research.

One “cautionary tale” that has been published involves a secondary analysis of the impact of a mood management intervention embedded in an online smoking cessation randomized control trial. Participants came to the online site in order to stop smoking. Half of the sample was randomly assigned to a cognitive behavioral mood management intervention. The authors wondered if smokers at risk for major depressive episodes (defined as having subthreshold levels of major depression symptoms at baseline) who had been randomly assigned to receive the mood management intervention had lower incidence of major depressive episodes at follow up, thus showing a preventive effect. The results showed that the incidence was actually significantly greater for the group assigned to the mood management condition (90). The authors speculate that being assigned to a mood management intervention when one is not looking for such might make a participant more aware of depressive symptoms, thus increasing their self-report scores at follow-up assessments. They then examined a subsequent study in which participants in a similar online smoking cessation trial could freely choose (rather than being randomly assigned to) the elements of the interventions provided, including the same mood management intervention. In this study, participants screening positive for major depression were more likely to choose the mood management intervention, and, if they did so, were more likely to quit smoking (91).

These studies suggest that providing participants with a choice of interventions may be preferable to assigning them interventions that they did not choose and that participants are likely to choose appropriate interventions for themselves.

All of investigated interventions were delivered over the Internet, only one study additionally used the mobile phone to facilitate the transfer of learned skills in daily life routine (48). None of the studies were delivered mobile only and none of the interventions’ used smartphone sensors, wearables, or artificial intelligence algorithms. It seems that the field so far focused mainly on delivering psychological intervention as a (guided) self-help format through the Internet, without making full use of the technological potential of such approaches. Artificial intelligence algorithms based on user behavior might bare a great potential for supporting participants to change behavior, for example, in form of just-in-time adaptive intervention (92). However, their potential with regard to preventing MHD onset still needs to be proven and should be investigated in future studies.

One general problem in the field of prevention is identifying the right people to target with specific preventive interventions. Many, if not most, individuals displaying single risk factors stay disease free without intervening, which has also occurred in most of the trials reviewed above. The combination of several specific risk factors might be a promising strategy toward overcoming this problem (93), and has been utilized in some first trials (58). Recent advantages in the field of precision medicine and machine-learning techniques might further help to identify the people at highest risk who might profit from a preventive intervention. There have been first studies in the mental health field predicting MDD onset (94, 95) in the general population, general anxiety and panic onset in general practice attendees (96) panic recurrence (97), or recurrence of suicidal ideation (97). A next necessary step in the field is to develop and validate such algorithms further for different populations and then, subsequently, test whether applying these risk prediction algorithms with subsequent preventive interventions is effective in reducing incidence of MHD in these high-risk groups.

The use of supervised machine-learning methods could also be used to explain the heterogeneity in intervention response (98, 99) in order to finally match individual to specific interventions. The necessary large sample sizes to develop such prediction equations are feasible to obtain with scalable IMIs.

A high-risk group that has been noticeably absent from preventive IMI trials is the group of individuals with genetic markers associated with mental disorders. A recent study (100) suggests a method that could combine genetic research with IMI depression prevention research. A massive sample collected by the genetic screening company was used to identify 15 genetic loci associated with risk of major depression: 75,607 individuals reporting clinical diagnosis of depression were compared to 231,747 individuals reporting no history of depression. A replication data set (45,773 cases and 106,354 controls) was then used to confirm the findings.

This very large sample could be the basis for an interesting set of depression prevention studies. Subgroups of individuals who have no history of depression but have the genetic markers associated with risk could be recruited for randomized trials testing promising IMIs. Such studies could contribute to prevention science in several ways by helping to confirm whether individuals with the markers actually have higher risk for developing major depressive episodes, particularly when confronted with stressful life events. They could also determine prospectively which of the 15 loci yield the greatest risk and help determine whether interventions that can be conducted online can reduce incidence in individuals with genetic loading for depression.

The use of IMIs allows the provision of tailored interventions on large scale to a degree unlikely ever to be implementable using traditional FTF approaches. So far, most studies evaluated standardized interventions based on CBT, including standard packages (e.g., cognitive restructuring, behavioral activation, relaxation in a depression prevention intervention, etc.) for all participants regardless of the individual specific risk or protective factors, as well as intervention preferences. One potential next step for the field would be to develop and provide intervention modules that target specific underlying risk or protective factors (e.g., rumination, emotion regulation, social skills, experiential avoidance, compensatory behavior anxiety sensitivity, and physical activity) and tailor the combination of these modules based on the individual risk and need profiles of the participant. However, whether such an approach is superior to far more simple to develop and maintain standardized interventions that provide non-tailored to all participants is an empirical question that has yet to be adequately addressed.

The impact of evidence-based preventive intervention on population level incidence heavily depends on the acceptance and use of such interventions in the target population (101). As the field of MHD prevention continues to make progress in identifying programs that yield positive effects, it will be important to develop and evaluate effective and cost-effective strategies to disseminate evidence-based programs. Hence, there is a need for studies that investigate potential obstacles in order to subsequently develop and evaluate strategies for overcoming them. For example, Ebert, Baumeister, and colleagues evaluated acceptance—facilitating interventions that address potential barriers for acceptance of IMIs in different target populations in a series of randomized controlled trials (102–104). Moessner and colleagues evaluated the effectiveness and cost-effectiveness of different school-based dissemination strategies for the prevention and early intervention in EDs (105). However, such experimentally examined strategies to increase the reach of preventive interventions are scarce and need to be conducted more often in order to fully exploit the potential of preventive interventions on the population level.

Internet- and mobile-based interventions are flexible, technically diverse methods which lend themselves to a variety of application areas. Such approaches have an ability to reach target groups in a way not yet achieved by classical FTF activities. A number of studies have shown that such interventions can be effective in preventing mental disorders. But clearly much more research is needed in order to fully determine the potential of IMIs for substantially reducing the immense disease burden of MHD at the population level.

DE drafted the first draft of the manuscript, all authors contributed to the further writing of the manuscript.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer KW and handling editor declared their shared affiliation, and the handling editor states that the process met the standards of a fair and objective review.