City Research Online

Abstract

Background: Digital interventions targeting common mental disorders (CMD) or CMD symptoms are fast-growing and gaining popularity, probably in response to the increased prevalence of CMD and better awareness of early help-seeking and self-care. However, no previous systematic reviews focusing on these novel interventions were found.

Objectives: This systematic review aimed to scope entirely web-based interventions which provided screening and signposting for treatment, including self-management strategies, for people with CMD or sub-threshold symptoms. In addition, a meta-analysis was conducted to evaluate the effectiveness of these interventions for mental wellbeing and mental health outcomes.

Methods: Electronic databases (MEDLINE, PsycINFO, CINAHL, EMBASE, CENTRAL, Web of Science, ASSIA, DARE, HTA, and NHS EED) were searched from 1st January 1999 to early April 2020. We included randomised controlled trials (RCT) which evaluated a digital intervention (1) targeting adults with common mental health disorder symptoms, (2) providing both screening and signposting to other resources including self-care, and (3) delivered entirely through the internet. Intervention characteristics including target population, platform used, key design features, and outcome measure results were extracted and compared. Trial outcome results were included in a meta-analysis on the effectiveness on users’ wellbeing and mental health outcomes. Health economic data were used to compile cost-effectiveness analysis. We also rated the meta-analysis results with GRADE to establish the quality of the evidence.

Results: The electronic searches yielded 21 papers describing 16 discrete digital interventions. These interventions were investigated by 19 unique trials including one health economic study. Most studies were conducted in Australia and North America. Populations targeted varied from the general population to allied health professionals. All interventions offered algorithm-driven screening with measures to assess symptom levels and to assign treatment options including automatic online psychoeducation, self-care strategies, and signposting to existing services. Meta-analysis of usable trial data showed that digital interventions improve wellbeing (3 RCTs, n = 1307, SMD 0.40, 95% CI 0.29 to 0.51, I2 = 28%, fixed effect), mental illness symptoms (6 RCTs, n = 992, SMD -0.29, 95% CI -0.49 to -0.09, I2 = 51%, random effects) and work and social functioning (3 RCTs, n = 795, SMD -0.16, 95% CI -0.30 to -0.02, I2 = 0%, fixed effect) comparing to waitlist or attention-control. However, scarce follow-up data failed to show any sustained effects beyond the post-intervention timepoint. Data on mechanisms of change and cost-effectiveness was also lacking, precluding further analysis.

Conclusions: Digital mental health interventions to assess and signpost people experiencing CMD symptoms appear to be acceptable to sufficient number of people and to have enough evidence for effectiveness to warrant further study. We recommend future studies incorporate economic analysis and process evaluation to assess mechanism of actions and cost-effectiveness so to aid scaling up implementation.

Publication Type:	Article
Publisher Keywords:	Digital health; mental wellbeing; common mental illness; depression; anxiety; self-care
Subjects:	B Philosophy. Psychology. Religion > BF Psychology Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Departments:	School of Health & Psychological Sciences > Nursing
SWORD Depositor:	Symplectic Administrator

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