Abstract
BackgroundEcological momentary assessments (EMAs) and ecological momentary interventions (EMIs) represent a 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 Communication Technologies (ICT) enabled researchers to bring all the needed processes together in only one device, i.e., response signaling, 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 applied in 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 major depressive disorder (MDD), with the aim of leading the way to possible future directions for the clinical practice.MethodsWe will conduct a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-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will include two bibliographic databases, PubMed and Web of Science (Web of Knowledge), supplemented by searches for unpublished or ongoing studies.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 data for adult (≥ 18 years old) with a primary (both current and past) diagnosis of MDD, defined by a valid criterion standard. We will consider studies adopting technology-based EMAs and EMIs for the investigation and/or assessment of depression and for the delivery of a psychological intervention. We will exclude studies adopting paper-and-pencil tools.DiscussionThe proposed systematic review will provide new insights on the advantages and benefits of adopting technology-based EMAs and EMIs for MDD in the traditional clinical practice, taking into consideration both clinical and technological issues. The potential of using sensors and biosensors along with machine learning for affective modeling will also be discussed.
Highlights
Ecological momentary assessments (EMAs) and ecological momentary interventions (EMIs) represent a novel approach for the assessment and delivery of psychological support to depressed patients in daily life
Ebner-Premier and colleagues conducted a systematic review on EMAs in the field of mood disorders and mood dysregulation, including major depressive disorder (MDD), bipolar disorder (BD), and borderline personality disorder (BPD) patients [16] to show how EMAs can better address research questions compared to laboratory or questionnaire studies
There still exists a huge gap between the clinical practice and the research field, and the Author(s), Sample(s), Variable(s), Device(s), Sensor(s), Duration, Prompt(s) per day, Sampling Schema, Primary Outcome(s); EMI
Summary
We will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines [36]. Ecological momentary intervention Data extracted from EMI studies will be the following: Study characteristics: aim of the study, type of electronic device, adoption of sensors and biosensors, sampling methods, duration and intensity of the treatment, type of data analysis (i.e., use of machine learning techniques); Participant: number of participants, control group, inclusion/exclusion criteria, dropout rates; Outcome measures: unit of measurement, type of measurement, successfulness of the intervention, compliance, participants’ satisfaction. As for EMA studies, we will focus both on technological (i.e., devices for intervention delivery, sensors, and biosensors) and technical features (i.e., sampling schemas, duration), as well as on clinical outcomes (i.e., content of the intervention, clinical improvement, patients’ satisfaction/feedbacks, compliance, and dropout rates) This process will help us to identify the key characteristics of a successful intervention, and highlight possible pitfalls that could be improved. As systematic review is an iterative process, this result schema could be redefined as the work progresses
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