Current state and future directions of technology-based ecological momentary assessments and interventions for major depressive disorder: protocol for a systematic review

Abstract

Background:Ecological momentary assessments (EMAs) and ecological momentary interventions (EMIs) representa novel approach for the assessment and delivery of psychological support to depressed patients in daily life.Beyond the classical paper-and-pencil daily diaries, the more recent progresses in Information and CommunicationTechnologies (ICT) enabled researchers to bring all the needed processes together in only one device, i.e., responsesignaling, repeated symptom collection, information storage, secure data transfer, and psychological support delivery.Despite evidence showing the feasibility and acceptability of these techniques, EMAs are only beginning to be appliedin real clinical practice, whether the development of EMIs for clinically depressed patients is still very limited.The objective of this systematic review is to provide the state of the art of technology-based EMAs and EMIs for majordepressive disorder (MDD), with the aim of leading the way to possible future directions for the clinical practice.Methods:We will conduct a systematic review using the Preferred Reporting Items for Systematic Reviews andMeta-Analysis (PRISMA) guidelines. Data sources will include two bibliographic databases, PubMed and Web of Science(Web of Knowledge), supplemented by searches for unpublished or ongoing studies. Eligible studies will report datafor adult (≥18 years old) with a primary (both current and past) diagnosis of MDD, defined by a valid criterionstandard. We will consider studies adopting technology-based EMAs and EMIs for the investigation and/or assessmentof depression and for the delivery of a psychological intervention. We will exclude studies adopting paper-and-penciltools.Discussion:The proposed systematic review will provide new insights on the advantages and benefits of adoptingtechnology-based EMAs and EMIs for MDD in the traditional clinical practice, taking into consideration both clinicaland technological issues. The potential of using sensors and biosensors along with machine learning for affectivemodeling will also be discussed.