eHealth interventions for the prevention of depression and anxiety in the general population: a systematic review and meta-analysis

Anxiety and depression, often termed ‘common mental disorders’ (CMD) because of their high prevalence rates in the general population [1], are associated with a substantial loss of quality of life for patients and their relatives [2, 3], increased mortality rates [4], high levels of service use, and enormous economic costs [5‐8]. Major depression is currently the fourth leading cause of disease burden worldwide, and is expected to rank first in disease burden in high-income countries by the year 2030 [9].

To date, most of the effort to reduce the burden of these disorders has been targeted at ensuring treatment is given to those with existing disorders. Although effective treatments are available, cost-effectiveness models suggest that even in the unlikely event of optimal treatment being delivered to all cases, only 35 to 50% of the overall burden of common mental disorders would be alleviated [10]. As a result, policy makers and researchers have begun to consider strategies aimed at prevention [11, 12].

To date, however, there has been little consensus about what preventive strategies would be both effective and feasible to roll out to whole populations [13, 14]. Prevention programs can be universal (directed at an entire population), selective (only those at high risk), or indicated (only those with emerging symptoms) [15]. The relative effectiveness of the different types of prevention as they relate to mental health remains unclear. Additionally, levels of CMD symptomatology are often treated as proxy for the disorder itself (where full diagnostic interviews are not conducted) and, as such, prevention efforts in many cases target symptom reduction as a primary outcome with the view that reduction in symptomatology is likely to both reduce incidence and have positive effects on overall wellbeing and quality of life [16]. Although, it is now well accepted that it should be possible to prevent at least some new cases of CMD [17‐19], it is important to consider the full spectrum of prevention models in evaluation. However, the cost associated with delivering most face-to-face psychological prevention programs has made large scale roll-outs unfeasible. The advent of technological innovation and eHealth interventions provides an opportunity to offer accessible, acceptable, easily disseminated low-cost interventions on a wide scale. It is known that certain eHealth interventions can have positive effects on symptoms of depression [20‐23] and anxiety disorders [24, 25] among those with clinically relevant symptom levels. However, much less attention has been given to preventative interventions in this area. Similarly, in most eHealth trials no ceiling cut-offs are applied to inclusion criteria, making it difficult to determine how such interventions perform as prevention. Unwell populations are likely to differ in terms of motivation or responsiveness to intervention, compared to at-risk, subclinical, and general populations. As such, there is a need to look specifically at these non-clinical populations to precisely understand effective prevention interventions.

The aim of this systematic review and meta-analysis is to examine the effects of eHealth psychological interventions aimed at participants without clinically diagnosable common mental disorders on reduction of anxiety and depression symptoms and incidence. We also examined the relative effects of universal, selective/indicated prevention programs.

This is the first systematic review and meta-analysis examining randomized controlled trials of eHealth interventions aimed at preventing depression and anxiety in the general population. Our results were drawn from ten randomised controlled studies with fair to good quality. Results indicate that a range of different cognitive behavioural programs produce small, but overall positive effects on symptom reduction for anxiety and depression, at both an indicated/selective and universal prevention level. Although the effect sizes among eHealth prevention interventions appear to be smaller than that reported for face-to-face prevention interventions [53], eHealth has potential for more reach with fewer resources. Indeed, eHealth technologies may be able to overcome a range of financial, geographic, and time barriers that have previously existed in this area. As such, these symptom effects may have considerable impact at a population level. Furthermore, considering the prevalence [54] and impairment associated with even subsyndomal disorders [55‐57], this effect has the potential to make meaningful change. Prevention interventions are never likely to produce large individual effect sizes, as they are delivered to the mass populations who are not unwell, but when translated to a population level, the overall impact of these small effects can be substantial and result in dramatic improvements in public health outcomes [58]. The eHealth prevention interventions included in this meta-analysis produced similar pooled mean effect sizes for both depression and anxiety outcomes. This may be a consequence of the overlap in symptoms between these conditions [59] and the lack of diagnostic outcomes used. The majority of studies utilised some form of CBT, with behavioural, mindfulness, and problem-solving components often a major focus. It is consistently shown that eHealth interventions that include some form of guidance are associated with larger effect sizes than those that do not [60], interestingly, the degree of support provided for this sample was relatively minimal, and the heterogeneous nature of this support prohibited further exploration. Although reminders were consistently used in the studies reviewed, few provided any form of therapist feedback. This is of particular relevance when considering adherence rates and overall effectiveness, as subclinical populations (such as those studies here) have been said to lack intrinsic motivation for completing the programs evoked by a diagnostic treatment imperative [61]. Despite the relatively minimal guidance provided among the prevention studies, program adherence was moderate across the studies and not dissimilar to that of clinical studies [62, 63].

Overall, similar effectiveness was found across the universal and selected/indicated eHealth prevention interventions. Although there is some evidence to suggest selected/indicated interventions might be more successful that universal approaches [64, 65], the majority of published studies in this area utilise school-based populations. Furthermore, many universal interventions may not be appropriately powered to find such effects as the sample size required for universal prevention can be overwhelming [66, 67]. The findings presented here are in line with those of Jane-Llopis and colleagues [68], and cast some doubt on the categorical superiority of selected/indicated interventions within the general population.

What is evident from this review is not only the lack of true eHealth prevention trials published in the literature [17, 69], but of those trials that do specifically target a non-clinical sample (be it universal, selective, or indicated prevention), the primary (and in most cases the only) relevant outcome is short-term symptom reduction rather than a decrease in disorder incidence. Three depression studies [40, 41, 52] and one anxiety study [43] reported medium to long-term follow-up, with 12-month incidence rates reported for only two studies [41, 52]. While each of the studies that examined depression diagnosis as an outcome reported significant differences at 12-months, when both were included in a meta-analysis the overall effect fell short of statistical significance. As such, there is inadequate evidence to suggest that eHealth interventions have an effect on long-term outcomes (especially for anxiety) and disorder incidence rates.

One of the key issues in conducting prevention trials is the sample sizes required to gain sufficient statistical power. For instance, it has been suggested that to demonstrate that a universal prevention program could reduce the rates of new onset depression over 12 months by 15%, the number of subjects required amounts to tens of thousands [13]. Furthermore, accurate diagnostic-based prevention within automated eHealth interventions is rare as this requires assessment contact with research staff, diminishing some key benefits of eHealth interventions (anonymity, low cost). Finally, the mental illness prevention field is relatively new [15], and although there has been promising results on the feasibility of prevention as a way of reducing the incidence and overall burden of common mental disorders at a community level, eHealth is also in its infancy [17].

The main strengths of this review include a detailed systematic search strategy and the objective assessment of the methodological rigor of each included study. Despite these strengths, there are a number of other limitations to this review. First, due to the limited number of studies, comparison of different theoretical approaches, different forms of prevention (universal, selective, or indicated), and other elements was difficult. Indeed, although this meta-analysis reported similar effectiveness among the universal and indicated/selective eHealth prevention interventions, there are classification issues that make interpretation difficult. For instance, due to the opt-in nature of research trials it is possible that the universal programs do not accurately reflect true universal prevention, as those taking part in such studies may have had symptoms of concern, which provided motivation for initial engagement. Conversely, some selective interventions of non-generalised populations were excluded from the review (e.g., postnatal, chronic pain), as they are often associated with fundamental differences which are likely to preclude the translation of such programs to other populations. Additionally, it would have been advantageous to make specific comparisons of differing theoretical approaches in order to determine which held most effectiveness in prevention, again limited numbers precluded this exploration. Similarly, the limited number of interventions and different types of techniques used may explain the heterogeneity observed in the data. The high levels of heterogeneity suggest treatment effect estimates vary among the studies, which may be due to different intervention content, engagement or difference in the populations sampled. While we utilised random effect models to help account for this, the remaining heterogeneity places some limitations on the interpretation of the pooled effect size, which may not be an accurate summary of the effects in all interventions. Secondly, as mentioned, self-report measures were the primary outcome in all studies, and therefore, conclusions are largely limited to reductions in symptoms rather than clinical diagnosis. Thirdly, as study populations were randomized, we conducted the meta-analysis under the assumption that there was no pre-test difference in scores for the control and treatment groups. Most studies reviewed tested for this and reported that no significant differences were present in the pre-test scores; however, in studies that did not, or where subgroups were used, this assumption may not hold [47]. Finally, as non-English publications and non-published articles were excluded, there is a possibility that such studies published in other languages or in the grey literature may have not been identified.

Although beyond the scope of this review it is important to note that one final limitation in evaluating effective prevention in this area is that of the search strategy [70]. The search specifically aimed to look at mental health disorders, however, this may have omitted relevant studies of wellbeing, or other less diagnostic studies (e.g., [71]). In short, these studies tend to measure reductions in the risk of illness by measuring symptoms of illness. In doing so the prevention techniques used were all ‘therapeutic’ (i.e., treatments intended to relieve or heal a disorder). This is a limitation across the clinical field of mental health prevention, as conditions lie on a spectrum and there is no accepted modifiable risk marker that is indicative of future illness. It would be useful to better understand the impact of CMD prevention interventions that do not target symptoms and whether other (non-therapeutic) techniques are effective (e.g., [72]). However, there is scepticism around the efficacy of wellbeing interventions as prevention [73].

Again, although beyond the scope of this review, a popular setting for prevention interventions is within school settings [74‐77], due to the advantages of access and ease with which such programs can be incorporated into the curriculum. Modern workplaces are becoming increasingly aware of the cost and impact of mental illness at an employee level, and initiatives in this area may lead to the workplace becoming an equivalent location where working-age adults can be engaged [33, 78].

In conclusion, this systematic review and meta-analysis found small but positive effects of eHealth prevention interventions for anxiety and depression. However, there is inadequate evidence on the medium to long-term effect of such interventions, and importantly, on the reduction of incidence of CMD. As little variation existed in the theories and techniques used it would be useful to explore other preventive strategies in eHealth delivery, (e.g. behavioural or mindfulness approaches), particularly those that have shown good effects in face-to-face sessions. Further work is needed to ascertain appropriate settings for such prevention work and to further explore the impact of eHealth psychological interventions on long-term incidence rates.