Improving the quality and reporting of evidence for digital health interventions for maximum population-level impact: a meta-review

Adrienne O'Neil, Fiona Cocker, Patricia Rarau, Shaira Baptista, Mandy Cassimatis, Barr Taylor, Annie Lau, Nitya Kanuri, Brian Oldenburg

Research output: Contribution to journalMeeting abstract


Introduction: The rapid development of digital health has resulted in poor reporting and quality of the scientific evidence. Many interventions remain under-evaluated, with a dearth of evidence of their wider, population level impact.
Methods: Between 2001 and 2015, we undertook a meta-review to describe, synthesize and evaluate (i) the effectiveness of digital health interventions for the prevention or control of cardiovascular disease, type 2 diabetes and/or depression; and (ii) their population impact using 8 parameters identified in the CONSORT-eHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile HEalth Applications and onLine TeleHealth) guidelines.
Results: Thirty three systematic reviews were included. Effectiveness data were most consistent for mobile health interventions. With respect to population impact: 3% reported comprehensive data on program development, 27% reported comprehensive data on participant program access, 45% provided a comprehensive description of the program being evaluated (model, theory, content, communications channels, prompts), 30% provided indication of
where supplementary resources, 15% detailed data collection and storage process such as security, 6% provided a detailed flow chart /diagram of usage, dose, engagement, 48% presented comprehensive data on demographics related to digital divide, and 36% reported comprehensive process outcomes.
Conclusions: Digital health interventions reported in reviews seldom considers parameters beyond effectiveness such as cost-effectiveness, feasibility to scale, and sub-population specific delivery. Their population level impact is likely to be of limited translational value in the absence of such data or standardized protocols that enable consolidation of these data.
Original languageEnglish
Article numberP520
Pages (from-to)S169
Number of pages1
JournalInternational Journal of Behavioral Medicine
Issue numberSuppl. 1
Publication statusPublished - Nov 2016


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