Publications by authors named "Outi Saleva"
Article Synopsis
- The study examines how behavioral data from smartphones can be used to detect and monitor depression symptoms in patients.
- Researchers collected smartphone data from 164 participants over a year, including both healthy individuals and patients with various depressive disorders.
- The analysis revealed 32 key behavioral markers linked to depression, achieving an 82% accuracy in classifying depressed individuals and 75% accuracy in tracking changes in their depressive states.
Background: Depression-related negative bias in emotional processing and memory may bias accuracy of recall of temporally distal symptoms. We tested the hypothesis that when responding to the Patient Health Questionnaire (PHQ-9) the responses reflect more accurately temporally proximal than distal mood states.
Methods: Currently, depressed psychiatric outpatients (N = 80) with depression confirmed in semi-structured interviews had the Aware application installed on their smartphones for ecological momentary assessment (EMA).