BACKGROUND
As information and communication technologies are increasingly integrated to enhance care quality, digitalization is transforming health systems. Digital health tools offer promising solutions within mental health, particularly in the treatment of depression, which currently affects 5% of the global population and has been predicted to be the leading disease burden by 2030. Primary care, often a patient’s first contact with healthcare, plays a critical role in the treatment of depression, especially with the increased demand for mental health care since COVID-19. Digital tools present significant potential to improve care accessibility and efficacy in primary care settings.
OBJECTIVE
The aim of this study is to assess the efficacy of digital health tools for the management of depression within primary care.
METHODS
A systematic review and meta-analysis of the literature was carried out following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies that recruited adult patients with depressive symptoms or a diagnosis of depressive disorder, treated with a digital intervention, and evaluated the effectiveness of a digital health tool were eligible. The primary outcome was identification of the digital health tool used for the intervention and the reduction of depressive symptoms. Only controlled trials were included in the review. The risk of bias in the original randomized controlled trials (RCTs) was assessed with version 2 of the Cochrane risk-of-bias tool for randomized trials, while non-RCTs were evaluated using the Joanna Briggs Institute (JBI) critical appraisal checklist for quasi-experimental studies.
RESULTS
A total of 29 controlled trials met the inclusion criteria. The digital health tools identified were web-based platforms, mobile apps, phone calls, text messages, and decision algorithms. A random effects meta-analysis was used to assess the efficacy of interventions in reducing depressive symptoms. All studies were rated as having some concerns regarding the risk of bias, or even a high risk of bias, especially due to the inability to blind patients, participants, and assessors. The most common digital intervention was cognitive-behavioral therapy. The meta-analysis revealed that digital health tools had a significant effect on depressive symptoms compared with control groups (g = -0.22, 95% CI: -0.37; -0.06, I2 = 79.64%). At 6 to 12-month follow-up, the random effects meta-analysis showed that digital health tools had a significant effect on depressive symptoms compared with control groups (g = -0.19, 95%CI: -0.29; -0.09, I2 = 53.42%). Post-intervention subgroup analyses based on the severity of depressive symptoms at baseline (p = 0.878) and type of intervention were non-significant (p = 0.110). A significant inverse relationship was observed between gender and effect size (B = -0.02, p = 0.022). Post-intervention meta-regression using mean age as a moderator demonstrated a trend toward significance (B = -0.02, p = 0.064).
CONCLUSIONS
Digital health tools, the majority of which are based on cognitive behavioral therapy, are effective in reducing the symptoms of depression, especially in combination. However, symptom severity does not predict suitability for digital treatment, and our findings highlight the need for gender-sensitive studies and strategies that will engage older adults. As digital interventions have yet to be included in clinical practice guidelines and treatment strategies, studies such as this are essential to support their integration into real-world practice.
