Research has largely shown the effectiveness of internet-based interventions for a wide range of mental health conditions. These studies have also shown that users find these interventions acceptable and easy to use. In this sense, internet-delivered interventions offer many advantages to the users, such as the geographic and time flexibility or the fact that they can access through different devices and the privacy associated with it.
Overall, research trials consider internet-based interventions as a single entity, so that they include measures before and after the intervention to explore potential changes. However, this approach does not account for how the interventions are actually adopted by the users and whether the level of program exposure is related with outcomes. In recent years, some studies are starting to report different metrics of platform usage, such as the time spent in the platform, the number of logins, number of activities completed, number of modules completed or percentage of program completion among others. There is a huge variability in the metrics reported by the studies and they use to report one or two metrics and not the rest. This lack of consistency when reporting the metrics might be biasing the findings, since it is not possible to ensure that they are not only reporting those metrics which show significant results.
The relationship between usage metrics and program outcomes have been explored in some studies and results are not showing consistency between them. In this sense, a review conducted in 2011 showed that only the degree of module completion and a summative outcome combining different metrics were related to outcomes. More recent studies have shown that number of activities completed per module, time spent and number of sessions were related to outcomes. Although these results are interesting and useful, they fail to demonstrate the dose-response relationship and they often rely on the assumption that “the more usage, the better”. This statement, although useful, does not allow to get a complete understanding of how these interventions work and a step further is needed in terms of defining benchmark levels of usage that can maximize the likelihood of getting a clinical improvement. Thus, the definition of usage thresholds for clinical improvement could, for example, allow to spot if a patient is likely to get a clinical improvement based on the use that he/she is doing from the platform.
As a whole, the exploration of the relationship between usage and outcomes is still in its infancy but it is attracting a lot of attention in the last few years. The results obtained in this regard will change the way we understand internet-based interventions for mental health and also how they are offered to patients. Ideally, big datasets where usage data and outcomes are combined would allow for machine learning and big data analyses that would allow to profile different type of users and different patterns of usage in order to make the interventions more responsive. In this sense, there are many potential uses that these data can have but it is only by investigating and finding out more about these variables and their relationships that we will be able to answer these questions.
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About the Author
Angel Enrique Roig is PhD in Psychology and he also has a postgraduate in the treatment of eating disorders. He currently works as a Clinical Researcher at Silvercloud Health Ltd and he also holds a research fellow position in the E-mental Health Research Group at Trinity College Dublin. His main background is in developing and testing the efficacy of clinical and positive psychological interventions, face -to-face and internet delivered, among different conditions, such as anxiety, depression and eating disorders. He has wide clinical experience as a psychotherapist attending patients with different mental health conditions. He is also very passionate about research and how technology can be used to improve access to mental health..