Electronic Thesis and Dissertation Repository

Thesis Format

Monograph

Degree

Master of Science

Program

Epidemiology and Biostatistics

Supervisor

Dr. Tarun Katapally

2nd Supervisor

Dr. Daniel Lizotte

Co-Supervisor

Abstract

Background: Mobile health (mHealth) interventions, deployed via digital devices like smartphones, have been employed to promote mental health literacy (MHL) by offering interactive, on-demand features that enhance knowledge, encourage help-seeking, and reduce stigma, while supporting scalable implementation.

Objective: To understand the impact of mHealth interventions on MHL compared to control conditions (non-mHealth interventions, waitlist, no intervention).

Methods: Five databases were searched for randomized controlled trials assessing mHealth interventions for MHL. Random-effects meta-analyses were conducted for overall MHL, knowledge, help-seeking, and stigma reduction. Risk of bias was assessed using the Cochrane RoB 2 tool.

Results: Compared to controls, mHealth interventions were associated with significantly higher overall MHL (SMD=0.10, 95% CI: 0.01 to 0.19), knowledge (SMD=0.23, 95% CI: 0.09 to 0.37), and help-seeking (SMD=0.18, 95% CI: 0.03 to 0.33) at post-intervention.

Conclusions: mHealth interventions were associated with higher MHL relative to controls, which may yield public health benefits when implemented at scale.

Summary for Lay Audience

Mental health literacy (MHL) is a key determinant of mental health that can help reduce the burden of mental disorders. It refers to the capacity to recognize, understand, and respond to mental disorders, including knowledge of conditions and services, willingness to seek help, and reduced stigma. Traditional MHL programs are typically delivered through structured formats like classroom sessions or static web modules, but often lack the resources and flexibility needed for sustained, interactive engagement. Mobile health (mHealth) interventions, accessed via everyday digital devices like smartphones, provide a promising and scalable alternative by offering interactive, timely, and personalized support that can be accessed as needed.

This systematic review and meta-analysis examined the impact of mHealth interventions on MHL. Fourteen randomized controlled trials (3,741 participants) were included. Participants aged 12 years and older were assigned to either an mHealth intervention or control condition (waitlist, non-mHealth intervention, or no intervention). Random-effects meta-analyses synthesized results while accounting for study-level differences like varying participant characteristics or measurement tools. Standardized mean differences were calculated to compare post-intervention MHL scores between intervention and control groups. MHL was assessed as a composite score (overall MHL), combining measures of mental health knowledge, help-seeking, and stigma reduction into a single summary estimate. Subgroup analyses examined effects across MHL domains (knowledge, help-seeking, stigma reduction), age groups (youth < 25 years, adults ≥ 25 years), and follow-up durations (≤ 3 months, > 3 months). Sensitivity analyses examined whether excluding studies with a high risk of bias or niche target populations affected the overall conclusions.

mHealth interventions were associated with modest but statistically significant higher overall MHL, knowledge, and help-seeking scores compared to controls at post-intervention assessment, with no statistically significant effect on stigma reduction. These differences were evident at follow-ups beyond three months but not at shorter durations. The direction and magnitude of effects remained consistent across sensitivity analyses.

Overall, mHealth interventions can support MHL promotion, particularly by being associated with higher knowledge and help-seeking scores. Although effect sizes were small and heterogeneity and risk of bias warrant cautious interpretation, the findings may hold meaningful public health value when implemented at scale.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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Available for download on Wednesday, July 01, 2026

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