Mental health disorders affect 13% of individuals worldwide (Walsh, 2022). Despite this, many people are unable to access treatment. There are various reasons as to why this is, for example, lack of evidence-based services and stigma (Gulliver et al., 2010). Digital mental health interventions can potentially overcome many of these barriers.
These types of interventions can be delivered through various modalities, including smartphone apps, video games and virtual reality (Mohr et al., 2013). A review combining results of various randomised controlled trials (the gold standard research methodology for evaluating efficacy of treatment) demonstrated that digital mental health interventions reduce anxiety symptoms (Firth et al., 2017).
Although the efficacy of these treatments has been shown to a large degree, there remains questions regarding engagement with digital mental health interventions. For example, people may struggle using technology and lack motivation to engage with a self-guided intervention (Wilhelmsen et al., 2013). Therefore, Judith Borghouts and colleagues (2021) carried out a systematic review assessing the barriers and facilitators of user engagement with digital mental health interventions.
The following databases were used to identify relevant studies: SCOPUS, PubMed, PsycINFO, Web of Science, and the Cochrane Library.
Articles met the following inclusion criteria:
- presence of an intervention aimed to improve mental health
- presence of an intervention delivered digitally
- reporting aspects of user experience
- reporting factors affecting user experience
- an empirical study
- participants aged 16 or older
- in English
Data extracted included reported barriers and facilitators to use and usage, the type of technology, whether the intervention was publicly available, the target population, and the length of time that participants were able to engage with the intervention during the study. The quality of the studies was assessed using a tool developed by Carroll et al (2012) which has been used in systematic reviews including both qualitative and quantitative studies.
An inductive thematic analysis was used to find common themes among identified barriers and facilitators.
208 studies were identified; 135 studies evaluated users’ experiences with a digital mental health intervention offered during a study, and 69 studies aimed to understand user needs and attitudes towards digital mental health interventions without or before engaging with a specific intervention as part of a study.
Regarding the quality of studies, all of them were deemed as adequately reported, as they reported the research question, study design and method of data collection. However, 11 studies did not indicate how they recruited/selected participants or specified the way they analysed the data.
Factors associated with user engagement were classified into 3 categories: user-related factors, program-related factors, and factors related to the technology and implementation environment.
1. User-related constructs
User-related factors referred to factors related to the user, such as demographic characteristics and personality traits. For instance, age, employment, neuroticism, and agreeableness were found to facilitate engagement in digital interventions, although extraversion was a barrier. High symptoms severity, participants’ literacy in understanding mental health, and using technology also facilitated engagement.
2. Program-related factors
Program-related factors referred to the type of therapy and content of the digital mental health intervention, in terms of perceived usefulness and fit. For instance, offering credible content in more than one modality facilitated engagement with digital mental health interventions. Moreover, offering content that was relevant, customisable, culturally appropriate, and with understandable language also facilitated engagement. Guided interventions were also found to have higher engagement than unguided interventions.
3. Technology-related and environment-related factors
Technology- and environment-related factors referred to factors related to the technology through which the intervention was provided. For instance, anonymity and sharing information privately were considered facilitators. Furthermore, providing training on how to use the digital mental health interventions and characterising them for well-being rather than mental health helped participants engage more. If participants were attending therapy, their therapist’s experience and attitude towards digital interventions also influenced their engagement.
This study classified factors affecting user engagement with digital mental health interventions into 3 main areas: (1) user characteristics, (2) users’ experience of the content, and (3) the technology and implementation environment. For example, offering relevant and customisable content along with accounting for individual user characteristics can enhance user engagement. These identified barriers and facilitators can guide the development of future digital interventions.
Strengths and limitations
This systematic review (Borghouts et al, 2021) pulls together available evidence on the factors influencing engagement with digital mental health interventions. However, there are limitations present in this review.
There was a lack of consensus on how to measure user engagement among studies, e.g., certain studies used the number of logins to an intervention whilst others used the total time spent using an intervention. It is understandable that digital mental health interventions created for different purposes need their own criteria to assess user engagement, however, this inconsistency impedes understanding and improvement of the current uptake of these interventions. Moreover, even though the review explored user engagement with both research interventions and commercially available ones, it is likely that certain interventions may have been overlooked. Although there is no meta-analysis to date on this topic when considering all relevant reviews (Fleming et al., 2018; Ng et al., 2019), the present review also did not conduct a meta-analysis as it included studies of different designs which increased heterogeneity. A meta-analysis is warranted to enhance the generalisability of the findings of individual studies.
Implications for practice
This comprehensive study provides information that can be taken forward to improve engagement in digital mental health interventions. For example, it was shown that anonymity was a facilitator in engagement. Therefore, developers should prioritise anonymity and make it clear to users how their data will be kept anonymous from the onset of accessing the intervention. Also, the study showed that providing training in how to use the intervention was a crucial element for users to engage; therefore, this should be paramount when developing the intervention and digital mental health interventions should therefore include a step-by-step guide of how to use the app.
Furthermore, the study also demonstrated that individuals with more severe mental illnesses were more likely to engage in digitally delivered interventions. Research has shown that those with serious mental health problems tend to struggle with engagement in traditional modalities of treatment (Dixon et al., 2016). Therefore, digitally delivered interventions could be an effective avenue to increase engagement with this client group. The government and mental health charities should consider prioritising digital interventions for those with, for example, severe depression or schizophrenia.
Statement of interests
Borghouts, J., Eikey, E., Mark, G., De Leon, C., Schueller, S. M., Schneider, M., … & Sorkin, D. H. (2021). Barriers to and facilitators of user engagement with digital mental health interventions: systematic review. Journal of Medical Internet Research, 23(3), e24387.
Carroll, C., Booth, A., & Lloyd-Jones, M. (2012). Should we exclude inadequately reported studies from qualitative systematic reviews? An evaluation of sensitivity analyses in two case study reviews. Qualitative Health Research, 22(10), 1425-1434.
Dixon, L. B., Holoshitz, Y., & Nossel, I. (2016). Treatment engagement of individuals experiencing mental illness: review and update. World Psychiatry, 15(1), 13-20.
Fleming, T., Bavin, L., Lucassen, M., Stasiak, K., Hopkins, S., & Merry, S. (2018). Beyond the trial: systematic review of real-world uptake and engagement with digital self-help interventions for depression, low mood, or anxiety. Journal of Medical Internet Research, 20(6), e9275.
Firth, J., Torous, J., Nicholas, J., Carney, R., Rosenbaum, S., & Sarris, J. (2017). Can smartphone mental health interventions reduce symptoms of anxiety? A meta-analysis of randomized controlled trials. Journal of Affective Disorders, 218, 15-22.
Gulliver, A., Griffiths, K. M., & Christensen, H. (2010). Perceived barriers and facilitators to mental health help-seeking in young people: a systematic review. BMC Psychiatry, 10(1), 1-9.
Mohr, D. C., Burns, M. N., Schueller, S. M., Clarke, G., & Klinkman, M. (2013). Behavioral intervention technologies: evidence review and recommendations for future research in mental health. General Hospital Psychiatry, 35(4), 332-338.
Ng, M. M., Firth, J., Minen, M., & Torous, J. (2019). User engagement in mental health apps: a review of measurement, reporting, and validity. Psychiatric Services, 70(7), 538-544.
Walsh, M. (2022, Februaty 15). Mental Health Statistics 2022. [Blog post]. Retrieved from https://www.singlecare.com/blog/news/mental-health-statistics/
Wilhelmsen, M., Lillevoll, K., Risør, M. B., Høifødt, R., Johansen, M. L., Eisemann, M., & Kolstrup, N. (2013). Motivation to persist with internet-based cognitive behavioural treatment using blended care: a qualitative study. BMC psychiatry, 13(1), 1-9.