RESUMEN
So far, several aspects of autobiographical memory (AM) have been found to be impaired in depression. Among others, depressed patients show the tendency to recall more negative than positive events (i.e., negative bias) and usually retrieve memories that lack of specificity and details (i.e., overgeneral memories). Based on this, we designed an AM task enhanced by the use of virtual reality (VR) to specifically train the recall of positive memories. Using a single-case, multiple baseline experimental design, we explored the effects of a brief intervention consisting of two sessions of this training in a sample of 18 individuals with moderate-to-moderately severe depressive symptoms. According to the results, changes occurred at the short term only. In particular, almost all participants reported a significant improvement in at least one outcome measure 0-3 days after the intervention. However, these clinical gains were not maintained in the mid-term (from day 4 to 10). The present findings do not support the efficacy of our VR-based AM recall treatment as a standalone intervention. Nevertheless, it might represent a suitable procedure to obtain immediate and/or short-term improvements. It might also serve as a valid component to be integrated in broader protocols for patients with moderate-to-moderately severe depressive symptoms.
RESUMEN
BACKGROUND: 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. 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. METHODS: We 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. DISCUSSION: The 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.
